How to Hire a Data Architect in 2026

Q: Your team is modeling a Customer hub in Data Vault 2.0. What belongs in the hub table itself?

Correct answer: A business key (or multi-part BK), a hash key derived from it, load timestamp, and record source — no descriptive attributes. Hubs store only business keys plus audit columns (hash key, load DTS, record source). Descriptive attributes live in satellites; relationships live in links. Putting PII/descriptives or metrics in the hub violates DV separation of concerns.

Q: You need point-in-time reporting across three Customer satellites with different load cadences. What construct do teams commonly add?

Correct answer: A Point-In-Time (PIT) table that snapshots the latest satellite load DTS (or hash diffs) per business key as-of reporting dates to avoid expensive multi-sat joins at query time. PIT tables pre-join satellite timelines for as-of queries. They're performance structures in business vault / mart prep, not a reason to multiply hubs or discard sats.

Q: Enterprise DV on a lakehouse: raw vault in Delta/Iceberg, business vault in the warehouse, marts in a semantic layer. Late-arriving satellite rows for a hub key appear with load DTS older than already-loaded rows. What's the sound approach?

Correct answer: Insert the late row with its true applied/load timeline, and ensure as-of/PIT logic uses business effectivity (or applied date) not only ingestion order so history re-sorts correctly. Late data is inserted; temporal logic must distinguish applied/effectivity time from pipeline load time. Overwrites and duplicate hubs break auditability.

Q: What approach do you take when tasked with learning a new technical concept or tool in Data Vault Modeling?

Why it matters: Evaluates learning agility and self-directed problem solving. What to listen for: Green flags: References official documentation, building prototype sandboxes, writing tests, and asking structured questions.

Q: Describe a complex technical bottleneck you resolved in Data Vault Modeling. What was the root cause?

Why it matters: Evaluates real-world optimization and diagnostics capability. What to listen for: Green flags: Uses profiling tools, isolates latency sources, measures before and after, and documents lessons learned.

Q: How do you evaluate architectural trade-offs when designing high-throughput systems in Data Vault Modeling?

Why it matters: Evaluates architectural pragmatism over hype-driven development. What to listen for: Green flags: Evaluates operational complexity, network latency, data consistency (CAP theorem), and organizational fit.

Q: A source sends product IDs as 'SKU-100' and another as '100'. How should Data Vault handle these for a Product hub?

Correct answer: Define a business-key definition (and optional same-as/link or staging BK consolidation) so both map to one enterprise product key before hashing into the hub. Hubs require a stable, agreed business key. Cross-source key collisions/format differences are resolved in staging or via same-as constructs so one hub key represents one business concept — not by arbitrarily discarding a source.

Q: Two source systems disagree on a customer's email. How should satellites be organized?

Correct answer: Separate satellites (or sat by source) so each system's attributes historize independently, then resolve conflicts in business vault / marts with explicit rules. Satellite-by-source (or rate-of-change) preserves each system's truth. Soft business rules choose a winner downstream without destroying raw history.

Q: You're choosing between end-dating satellites vs using load-DTS-only + current views. Zero-downtime 24/7 streaming CDC lands every few seconds. What's the production trade-off?

Correct answer: High-frequency end-dating updates create write amplification and contention; load-DTS insert-only with windowed 'latest' views (and periodic PIT rebuilds) usually scales better for streaming, at the cost of more complex as-of SQL. Streaming favors insert-only sats; continuous end-date updates fight the CDC firehose. PIT/materialized latest views restore query ergonomics.

Q: Describe a difficult bug you encountered in a recent project and how you solved it.

Why it matters: Evaluates systematic debugging process. What to listen for: Green flags: Uses logs, debuggers, isolation techniques, and root-cause analysis.

Hiring Workflow, Step by Step

Every stage of this hire can run in SkillSauce on one board, from the first applicant to the offer. The level sections below add the detail for junior (0-3 years), mid-level (3-6 years) and senior (6+ years) hires.

1
Open a Job Profile for the role
Create a Job Profile for the role: one Kanban board, free on every plan, that holds every candidate, test and interview for this hire. Write the job description with the free Job Description Generator, or start from the template in your level below.
2
Fill the candidate pool
Publish the job on your Career Page and share the link on LinkedIn, X and job boards, so every applicant lands on the board. To reach people who have not applied, search Candidate Lookup by domain and seniority, then narrow by industry, company and location. You can also import LinkedIn profiles by URL, upload resumes in bulk or import a CSV.
3
Shortlist the strongest resumes
Move applicants into the Resume Shortlister: TESSA AI scores every resume against the Job Profile and shows its reasoning, so you can see why each candidate made the cut before you send a single test.
4
Test the skills the role needs
Send the shortlist an IT assessment on Data Vault Modeling, Kimball Methodology and ER Modeling, with a coding test in any of 60+ languages. Build it from the Question Library of 320,000+ questions, or let TESSA AI generate it when your exact skill mix is missing, and keep AI proctoring on for remote candidates.
5
Interview with TESSA AI
Invite the candidates who pass to a TESSA AI interview on the role itself, in any of 100+ languages. Add a Culture Fit round to see how they work with a team like yours, and a Behavioural round for senior hires, where judgment and ownership matter most.
6
Meet the shortlist
Schedule the live technical round on Zoom or Google Meet with Google Calendar invites, and keep candidates posted on WhatsApp or email.
7
Decide together and make the offer
Compare finalists on their Reports: scored results for each test and AI interview, plus the proctoring activity of proctored tests. Collect your hiring team’s feedback on each candidate, then make the offer with the email templates in your level below.

Company Fit: Startups, SMBs and Enterprises

Startups

Startups need Data Architects who can move fast, build scalable foundations in Data Vault Modeling, and drive high feature velocity.

Rapid prototyping with modern Data Vault Modeling tooling.
High developer autonomy reducing operational overhead.
Fast feedback loops and continuous production releases.
Start free: the Starter plan includes 40 credits, about 10 AI interviews or assessments, with no time limit and no card needed.

SMBs

Clean modular codebases built with standard Data Vault Modeling conventions.
Lower maintenance overhead with automated CI/CD and monitoring.
Seamless integration with cloud-native infrastructure (Docker, Kubernetes).
One board per role: sourcing, resume shortlisting, assessments and TESSA AI interviews all run from a single Job Profile.

Enterprises

Enterprises rely on Data Architects for mission-critical, high-availability systems with strict compliance and security.

Enterprise-grade reliability handling high concurrency and strict SLAs.
Strict type safety and compile-time verification preventing runtime bugs.
Seamless integration with enterprise message brokers and cloud architectures.
Evidence in one place: scored reports for every test and AI interview, with your hiring team’s feedback beside them.

Junior level

Junior Data Architect

Experience: 0 - 3 Years
Average time to hire: 10 - 18 Days

Core tech stack

Data Vault Modeling
Kimball Methodology
ER Modeling
ELT (load-first pattern)
Git
Unit Testing

How to Hire a Junior Data Architect

When hiring an entry-to-junior Data Architect (0–3 years), focus on fundamental mastery of Data Vault Modeling, code readability, structured debugging, and coachability.

1
Define Core Role Scope
Identify key responsibilities around Data Vault Modeling, team collaboration, and tooling integration.
2
Sourcing Channels
Source candidates through Data Vault Modeling developer communities, tech forums, and GitHub repositories. Beyond applicants, Candidate Lookup finds people by domain and seniority, narrowed by industry, company and location, and you can add them to the same pipeline.
3
SkillSauce Technical & TESSA Screening
Administer a 30-minute practical assessment and a 15-minute TESSA AI conversational screen. Build it in IT Assessments from the Question Library, add a coding test in the language you hire for, and keep AI proctoring on. TESSA then runs the AI interview in any of 100+ languages.
4
Technical Pair Interview
Conduct a 45-minute live pair programming and problem-solving interview with senior engineers. Schedule it on Zoom or Google Meet with a Google Calendar invite, and send the reminder on WhatsApp.
5
Fast-Track Offer Rollout
Present a competitive offer with clear mentorship roadmap and structured onboarding.

Pro Tips

Evaluate practical understanding of core Data Vault Modeling concepts over memorized trivia.
Assess problem-solving methodology when encountering unfamiliar bugs or errors.
Verify ability to articulate technical decisions clearly during code walkthroughs.

Junior Data Architect Job Description Template

Ready-to-publish job description tailored for junior Data Architect positions.

Junior Data Architect Job Specification

Role Overview

We are looking for a motivated Junior Data Architect to join our engineering organization. You will collaborate with experienced engineers, write clean, well-tested code, and contribute to production deliverables in Data Vault Modeling.

First 90 Days Deliverables

Days 1–30: Complete development environment setup, learn team workflows, and resolve initial onboarding and bug-fix tickets.
Days 31–60: Implement core features in Data Vault Modeling, write automated unit tests, and participate in peer code reviews.
Days 61–90: Deliver end-to-end task features independently, contribute to system documentation, and participate in sprint planning.

Must-Have Requirements

0–3 years of hands-on experience or academic/project background in Data Vault Modeling.
Solid understanding of fundamental software engineering principles, testing, and debugging.
Familiarity with version control (Git) and collaborative team workflows.

Nice-to-Have Skills

Experience with cloud platforms (AWS, GCP, Azure) or containerization (Docker).
Contributions to open-source projects or tech community participation.

Need a version for another role or tone? Draft one with the free Job Description Generator.

Top Sourcing Channels for Junior Data Architect

Key channels and communities to source junior Data Architect talent.

SkillSauce

Candidate Lookup

Find people who have not applied yet. Search by domain and seniority, narrow by industry, company and location, and add them to the role’s Job Profile with their available contact details.

SkillSauce

Career Page and LinkedIn Import

Publish the job on your Career Page and share the link on LinkedIn, X and job boards: every applicant lands in the role’s Job Profile. Import a LinkedIn profile by its URL when you spot someone.

Professional network

LinkedIn (opens in a new tab)

linkedin.com

Search by title, skills and school, post the opening, and look for people sharing their projects in relevant groups.

Social network

X (Twitter) (opens in a new tab)

x.com

Post the role with hashtags such as #DataVaultModeling, and join the X Communities where people in the field share their work.

Developer Community

Data Vault Modeling Community Network

Active developer communities and channels discussing Data Vault Modeling.

Developer Community

Reddit Tech Hubs (opens in a new tab)

reddit.com

High-engagement technical subreddits with dedicated hiring threads.

Open Source Directory

GitHub Trending Projects (opens in a new tab)

github.com

Source contributors to open-source projects in Data Vault Modeling.

Recruitment Platform

Stack Overflow Jobs (opens in a new tab)

stackoverflow.com

Global developer platform connecting verified technical talent.

Job boards

Job Boards

Post the job on Indeed and Glassdoor, and on Naukri and foundit for hiring in India. Where a board allows it, use your Career Page link as the apply link, so applicants land in the same Job Profile.

Campus and referrals

Campus Placement Cells and Referrals

Partner with college placement cells and career services for graduates and interns, and ask your team who they studied or interned with.

Candidate Email Sequences for Junior Data Architect

High-converting candidate recruitment sequences.

Cold Sourcing Email

Subject: Junior Data Architect opportunity at [Company Name]

Show template

Hi [Candidate Name], I noticed your experience in Data Vault Modeling and wanted to reach out. We are expanding our team at [Company Name] and looking for a Junior Data Architect. Let's connect for an intro chat: [Booking Link] Best regards, [Your Name]

Technical Interview Invite

Subject: Interview Invitation: Junior Data Architect at [Company Name]

Show template

Hi [Candidate Name], Thank you for completing our initial assessment! We would love to invite you to a 45-minute technical conversation. Please select a convenient slot: [Scheduling Link] Best, [Your Name]

Formal Offer Letter

Subject: Offer of Employment: Junior Data Architect - [Company Name]

Show template

Hi [Candidate Name], We were very impressed by your problem-solving skills and technical foundation. We are delighted to offer you the role of Junior Data Architect at [Company Name]! Welcome to the team! Warmly, [Hiring Manager Name]

Junior Data Architect Assessment Blueprint

Automated technical evaluation and conversational screening.

30 Minutes

SkillSauce Data Vault Modeling Practical Coding Assessment

Candidates solve a practical problem, handle edge cases, and write clean unit tests.

Focus areas

Syntax & Control Flow
Problem Solving
Error Handling
Unit Testing

15 Minutes

TESSA AI Behavioral & Technical Screen

Automated conversational screen evaluating technical reasoning and learning capacity. TESSA AI runs it as a Behavioural round and returns a scored report for the hiring team.

Focus areas

Problem solving methodology
Learning agility
Communication

Why This Assessment Mix

Verifies core foundational competency in Data Vault Modeling and software engineering mindset.

You can run tests like these in SkillSauce: build an IT skills assessment from the test library and turn on AI proctoring for remote candidates.

Junior Data Architect Behavioral Rubric

Behavioral questions assessing problem solving and growth mindset.

What approach do you take when tasked with learning a new technical concept or tool in Data Vault Modeling?

Why it matters: Evaluates learning agility and self-directed problem solving.
What to listen for: Green flags: References official documentation, building prototype sandboxes, writing tests, and asking structured questions.

Describe a difficult bug you encountered in a recent project and how you solved it.

Why it matters: Evaluates systematic debugging process.
What to listen for: Green flags: Uses logs, debuggers, isolation techniques, and root-cause analysis.

How do you handle receiving critical feedback during a code review?

Why it matters: Evaluates coachability and team collaboration.
What to listen for: Green flags: Views feedback constructively, asks clarifying questions, and focuses on code quality.

TESSA, the SkillSauce AI interviewer, can run a structured first-round interview built around questions like these. See how TESSA AI interviews work.

Verified Technical Questions (Junior Tier)

5 verified Easy technical questions extracted directly from the SkillSauce question bank.

Your team is modeling a Customer hub in Data Vault 2.0. What belongs in the hub table itself?

Easy

aA business key (or multi-part BK), a hash key derived from it, load timestamp, and record source — no descriptive attributesCorrect
bCustomer name, address, and phone for SCD Type 2 history
cAll foreign keys to related satellites and links
dAggregated customer lifetime value metrics for BI

Explanation: Hubs store only business keys plus audit columns (hash key, load DTS, record source). Descriptive attributes live in satellites; relationships live in links. Putting PII/descriptives or metrics in the hub violates DV separation of concerns.

Why it matters: Evaluates practical understanding of Data Vault Modeling concepts and problem solving for Data Architect at the junior experience tier.

A source sends product IDs as 'SKU-100' and another as '100'. How should Data Vault handle these for a Product hub?

Easy

aPick one source as master and ignore the other
bDefine a business-key definition (and optional same-as/link or staging BK consolidation) so both map to one enterprise product key before hashing into the hubCorrect
cStore both as separate hubs forever
dConcatenate them into a satellite attribute

Explanation: Hubs require a stable, agreed business key. Cross-source key collisions/format differences are resolved in staging or via same-as constructs so one hub key represents one business concept — not by arbitrarily discarding a source.

Why it matters: Evaluates practical understanding of Data Vault Modeling concepts and problem solving for Data Architect at the junior experience tier.

What is the primary purpose of a link table in Data Vault?

Easy

aStore slowly changing descriptive attributes of an entity
bReplace the need for satellites entirely
cCapture the relationship (association) between two or more hubs as a unique set of hub hash keys, with load metadataCorrect
dMaterialize star-schema fact tables for BI

Explanation: Links model many-to-many (or n-ary) relationships by holding the participating hub hash keys. Descriptives of the relationship go in link satellites; hubs remain key-only; BI facts are often built later from DV structures.

Why it matters: Evaluates practical understanding of Data Vault Modeling concepts and problem solving for Data Architect at the junior experience tier.

Your satellite on Customer has attributes that change weekly. How does Data Vault typically track history?

Easy

aOverwrite the row in place (Type 1)
bDelete the old row and insert the new one
cKeep history only in the hub
dInsert a new satellite row with a new load timestamp (and end-dating or effectivity pattern) rather than updating history in placeCorrect

Explanation: Satellites are insert-only chronological records of attribute change. New loads append; current-state views use latest-by-hash or effectivity. Hubs don't hold descriptive history.

Why it matters: Evaluates practical understanding of Data Vault Modeling concepts and problem solving for Data Architect at the junior experience tier.

Why does Data Vault 2.0 favor hash keys over sequential surrogate keys for hubs and links?

Easy

aHashes enable parallel, independent loading without a central sequence generator and support deterministic key computation from business keys across pipelinesCorrect
bHashes are shorter than integers
cHashes guarantee no collisions ever
dDatabases cannot index integers efficiently

Explanation: Hash keys (MD5/SHA) let independent load jobs compute the same hub/link key from the BK without coordinating sequences — critical for parallel ELT. Collision risk is managed by algorithm choice; length and indexing myths are wrong.

Why it matters: Evaluates practical understanding of Data Vault Modeling concepts and problem solving for Data Architect at the junior experience tier.

Need more questions for this stack? Browse sample questions by skill in the SkillSauce Question Library.

Constructive Rejection Email Templates

Brand-protective rejection email templates.

Post-Assessment Rejection

Subject: Update on your application at [Company Name]

Show template

Hi [Candidate Name], Thank you for completing our assessment. At this time, we have chosen to move forward with candidates whose current background more closely matches our immediate requirements. Best regards, [Company Name] Team

Post-Interview Rejection

Subject: Interview Feedback: Data Architect at [Company Name]

Show template

Hi [Candidate Name], Thank you for taking the time to meet with our team. We were impressed by your passion for technology. At this stage, we have decided to extend an offer to another candidate. Warm regards, [Hiring Manager Name]

Mid-Level level

Mid-Level Data Architect

Experience: 3 - 6 Years
Average time to hire: 18 - 25 Days

Core tech stack

Data Vault Modeling
Kimball Methodology
ER Modeling
ELT (load-first pattern)
Lakehouse Architecture
Data Mesh
System Optimization
Docker

How to Hire a Mid-Level Data Architect

For Mid-Level Data Architect (3–6 years), evaluate modular architecture design, feature ownership, performance optimization, automated testing, and cross-functional collaboration in Data Vault Modeling.

1
Define Feature Ownership & Architecture Goals
Establish expectations for system scalability, feature delivery, and performance targets in Data Vault Modeling.
2
Targeted Mid-Level Sourcing
Source experienced professionals from specialized Data Vault Modeling networks and tech hubs. Share the job from your Career Page in these channels, and add people who have not applied from Candidate Lookup or by their LinkedIn profile URL.
3
SkillSauce Performance & Systems Assessment
Administer a 45-minute practical task testing optimization, error resilience, and automated testing. Pick the questions from the Question Library, or let TESSA AI generate them when your exact skill mix is missing, and keep AI proctoring on for remote candidates.
4
System Design & Code Review Interview
Conduct a 60-minute technical session reviewing architectural trade-offs and code maintainability. Share the candidate’s report with the interviewers beforehand, so the conversation starts from their test results.
5
Extend Competitive Offer
Present an attractive compensation package with high feature ownership and engineering autonomy.

Pro Tips

Test their ability to diagnose and eliminate performance bottlenecks in Data Vault Modeling.
Evaluate experience with automated CI/CD pipelines, containerization, and monitoring.
Assess practical knowledge of database optimization and error recovery patterns.

Mid-Level Data Architect Job Description Template

Comprehensive job specification for mid-level Data Architect roles.

Mid-Level Data Architect Specification

Role Overview

We are looking for an experienced Mid-Level Data Architect to build, optimize, and scale production systems. You will own core components, write clean modular code, and drive system reliability in Data Vault Modeling.

First 90 Days Deliverables

Days 1–30: Audit existing systems, identify performance bottlenecks, and resolve initial feature tickets.
Days 31–60: Design and implement modular components in Data Vault Modeling, write comprehensive integration test suites, and automate CI/CD workflows.
Days 61–90: Lead feature refactoring, optimize query/compute performance, and mentor junior engineers.

Must-Have Requirements

3–6 years of professional engineering experience with 2+ years of production experience in Data Vault Modeling.
Proven track record designing modular, scalable software architectures.
Strong experience with automated testing, CI/CD pipelines, and cloud environments.
Excellent technical communication and problem-solving skills.

Nice-to-Have Skills

Experience with microservices, event-driven systems, or high-throughput data pipelines.

Need a version for another role or tone? Draft one with the free Job Description Generator.

Top Sourcing Hubs for Mid-Level Data Architect

Key channels for recruiting experienced Data Vault Modeling professionals.

SkillSauce

Candidate Lookup

Search by domain and seniority, then narrow by the companies and locations you want to hire from. The people you pick join the same pipeline as your applicants.

SkillSauce

Career Page and LinkedIn Import

Publish the job on your Career Page and share the link on LinkedIn, X and job boards: every applicant lands in the role’s Job Profile. Import a LinkedIn profile by its URL when you spot someone.

Social network

X (Twitter) (opens in a new tab)

x.com

Post the role with the hashtags your field follows, and join the X Communities where people in the field share their work.

Developer Community

Data Vault Modeling Professional Network

Target experienced Data Vault Modeling developers actively building applications.

Developer Community

Hacker News: Who is Hiring? (opens in a new tab)

news.ycombinator.com

Monthly tech hiring thread attracting seasoned technical talent.

Open Source Directory

GitHub Active Contributors (opens in a new tab)

github.com

Source contributors to popular Data Vault Modeling packages and libraries.

Recruitment Platform

LinkedIn Advanced Search (opens in a new tab)

linkedin.com

Target mid-level engineers with proven Data Vault Modeling expertise.

Developer community

Stack Overflow (opens in a new tab)

stackoverflow.com

Find people who answer questions in the tags your stack uses: their answers show how they reason and explain.

Job boards

Job Boards

Post the job on Indeed and Glassdoor, and on Naukri and foundit for hiring in India. Where a board allows it, use your Career Page link as the apply link, so applicants land in the same Job Profile.

Referrals

Employee Referrals

Ask your team who they have worked with. Referred candidates take the same assessment and interview as everyone else.

Mid-Level Data Architect Candidate Email Sequences

High-converting candidate sequences.

Cold Sourcing Email

Subject: Engineering opportunity at [Company Name]

Show template

Hi [Candidate Name], I noticed your strong background in Data Vault Modeling. We're hiring a Mid-Level Data Architect to take ownership of high-impact features. Let's chat: [Booking Link] Best, [Your Name]

Technical Interview Invite

Subject: Technical Architecture Interview: Data Architect at [Company Name]

Show template

Hi [Candidate Name], Thank you for completing our technical assessment. We'd love to invite you to our 60-minute technical design conversation. Please pick a time: [Booking Link] Best, [Your Name]

Formal Offer Letter

Subject: Offer: Mid-Level Data Architect - [Company Name]

Show template

Hi [Candidate Name], We are delighted to offer you the position of Mid-Level Data Architect at [Company Name]! Welcome aboard! Sincerely, [Hiring Manager Name]

Mid-Level Data Architect Assessment Battery

Calibrated technical challenge testing system design and optimization.

45 Minutes

SkillSauce Data Vault Modeling Architecture & Performance Test

Candidates optimize a complex workflow, resolve performance bottlenecks, and write integration tests.

Focus areas

System Optimization
Data Integrity
Error Recovery
Automated Testing

20 Minutes

TESSA AI System & Architectural Screen

Evaluates candidate reasoning during production outages and architecture discussions. TESSA AI runs it as an AI interview and returns a scored report for the hiring team.

Focus areas

Incident response
Architecture trade-offs
Maintainability

Why This Assessment Mix

Verifies ability to manage system performance and service reliability in Data Vault Modeling.

You can run tests like these in SkillSauce: build an IT skills assessment from the test library and turn on AI proctoring for remote candidates.

Mid-Level Data Architect Behavioral Rubric

Behavioral questions assessing feature ownership and technical maturity.

Describe a complex technical bottleneck you resolved in Data Vault Modeling. What was the root cause?

Why it matters: Evaluates real-world optimization and diagnostics capability.
What to listen for: Green flags: Uses profiling tools, isolates latency sources, measures before and after, and documents lessons learned.

How do you balance technical debt reduction with shipping new feature requests?

Why it matters: Evaluates prioritization and pragmatic engineering mindset.
What to listen for: Green flags: Quantifies impact of technical debt on velocity, advocates for continuous refactoring, and aligns with product goals.

How do you ensure data integrity and error resilience across distributed components?

Why it matters: Evaluates systems design and error handling practices.
What to listen for: Green flags: Explains idempotency, transactional boundaries, retry mechanisms with exponential backoff, and alerting.

TESSA, the SkillSauce AI interviewer, can run a structured first-round interview built around questions like these. See how TESSA AI interviews work.

Verified Technical Questions (Mid Tier)

5 verified Medium technical questions extracted directly from the SkillSauce question bank.

You need point-in-time reporting across three Customer satellites with different load cadences. What construct do teams commonly add?

Medium

aA second Customer hub per satellite
bA factless fact with no keys
cA Point-In-Time (PIT) table that snapshots the latest satellite load DTS (or hash diffs) per business key as-of reporting dates to avoid expensive multi-sat joins at query timeCorrect
dDrop two of the satellites and keep one wide table

Explanation: PIT tables pre-join satellite timelines for as-of queries. They're performance structures in business vault / mart prep, not a reason to multiply hubs or discard sats.