What does a Data Architect do?
A data architect decides how an organisation's data is structured, stored and governed — canonical models, warehouse or lakehouse layout, naming and grain standards, lineage and access rules. In Indian enterprises the role usually arrives after years of point-to-point pipelines have produced conflicting versions of the same revenue number, and the mandate is one trustworthy definition per business concept.
A data architect usually has around 8–18 yrs of experience and earns typically ₹25L–₹70L/yr in India. The day-to-day blends Dimensional modelling, Data Vault, Snowflake / BigQuery / Databricks and more — this page gives you a ready-to-use data architect job description template you can copy, plus the exact skills and salary employers expect.
Data Architect job description template
Copy the 9 responsibilities, 6 requirements and 10 skills below into your job post, then edit the parts that are specific to your company — pay band, location and reporting line.
Select the text above to copy it manually if the button is unavailable.
What are a Data Architect's key responsibilities?
A data architect is typically responsible for the 9 duties below, which cover the day-to-day work most employers expect the role to own outright. Paste them into your job post as they are, or cut the ones another team already handles — a responsibility list that claims work the hire will not actually do is the fastest way to lose a candidate at offer stage:
- Define conceptual, logical and physical models plus the grain of every core fact table
- Choose warehouse, lakehouse or hybrid storage patterns and the table formats behind them
- Establish a business glossary so each metric carries one agreed definition
- Set up master data management for customers, products and vendors across source systems
- Design lineage, cataloguing and classification so any figure traces back to its source
- Write standards for ingestion, historisation, slowly changing dimensions and retention
- Define access tiers, masking and residency controls that satisfy privacy obligations
- Evaluate and standardise tooling, then review team designs against the reference architecture
- Plan phased migrations off legacy warehouses without freezing reporting for a quarter
What qualifications does a Data Architect need?
Employers hiring a data architect in India usually ask for the 6 qualifications below, typically alongside 8–18 yrs of experience. Keep only the ones you will genuinely screen on: every extra must-have narrows the pool, and in the Indian market a long mandatory list filters out strong candidates whose background simply reads differently on paper:
- Deep dimensional and normalised modelling experience, including Kimball and Data Vault trade-offs
- Hands-on background with a cloud warehouse or lakehouse such as Snowflake, BigQuery or Databricks
- Strong SQL plus familiarity with distributed processing and formats like Iceberg or Delta
- Practical knowledge of governance, cataloguing, lineage and data-quality frameworks
- Experience influencing engineering and business stakeholders without direct authority
- Awareness of Indian data-protection obligations and how residency shapes design
What skills should a Data Architect have?
These are the 10 skills employers name most often on live data architect listings in India. Treat the first few as the ones worth screening for directly and the rest as signals a candidate can pick up on the job — asking for all of them at once is what turns a reasonable role into an unfillable one:
Listing the three or four skills you genuinely screen on — rather than all 10 — is what keeps a data architect posting from filtering out candidates who could do the job.
What does a Data Architect earn in India?
Typical salary (India)
typically ₹25L–₹70L/yr
Experience range
8–18 yrs
These are typical ranges and vary by city, company and skills. For live, role-specific pay data, see the OnJob salary guide.
Data Architect job description — FAQs
What separates a data architect from a data engineer?
Data engineers build and run pipelines: ingestion jobs, transformations, orchestration and the reliability of what already exists. Architecture work decides what should exist — the models, storage layout, naming standards, governance rules and tooling every pipeline must follow. One optimises a single delivery; the other stops twenty deliveries from disagreeing with each other.
Warehouse or lakehouse — how is the choice made?
The decision follows workload shape rather than fashion. Structured, heavily governed reporting with predictable schemas runs cheaply on a warehouse. Large semi-structured volumes, machine-learning feature work and open table formats favour a lakehouse. Many Indian enterprises land on both: a lakehouse for raw and exploratory data, a curated warehouse layer for finance-grade reporting.
How do you get data governance adopted rather than ignored?
Governance sticks when it sits in the path of least resistance: definitions in a catalogue that BI tools read directly, quality checks that fail a pipeline instead of emailing a report, and ownership assigned to named people per domain. Policy documents alone change nothing. Starting with the few metrics executives argue about builds credibility to expand.
What experience leads to this role in India?
Data architects usually arrive after eight to fifteen years in data engineering, BI development or database work, having modelled several warehouses and survived at least one migration. The step up demands breadth — governance, security, cost, vendor evaluation — plus the communication to defend a standard to both a CFO and a sceptical engineering team.