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Data Engineer

at Brillio 2

Bangalore, Karnataka, India, onsite, mid, 5–8 years

posted 22 Sept 2026

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  • Airflow
  • dbt Cloud
  • Slack
  • Confluence
  • Snowflake
  • dbt
  • Jira
  • SQL
  • Python
  • Data modelling

About the role

Posted: 2026-09-22

Data Engineer

Job requirements Key Responsibilities Logic Discovery and Requirements Analysis

- Work with pricing analysts to identify custom definitions, calculations, transformations, dependencies, assumptions, edge cases, and ambiguities currently implemented in the Gold layer.

- Reverse-engineer analyst-written SQL, spreadsheet logic, and ad hoc calculations to define intended business behaviour.

- Translate legacy logic into clear technical requirements and migration plans.

Migration to the Silver Layer

- Re-implement approved pricing definitions as governed, reusable, documented dbt models in the Silver layer.

- Follow data modelling, naming, testing, version control, and code review standards.

- Build maintainable, scalable, auditable models independent of undocumented analyst knowledge.

- Apply dbt tests for uniqueness, not-null, accepted values, relationships, and business rules.

Parity Validation and Quality Assurance

- Compare migrated Silver-layer results with existing Gold-layer outputs and resolve differences.

- Validate results across representative periods, segments, boundary conditions, and known exceptions.

- Confirm migrations only after parity review and pricing analyst approval.

Analyst Collaboration and Sign-Off

- Partner with pricing analysts to clarify calculations, confirm intended behaviour, and align on expected results.

- Lead walkthroughs and reviews; document analyst approval before retiring legacy definitions.

- Communicate risks, open questions, dependencies, and decisions to technical and business stakeholders.

Documentation and Lineage

- Document business meaning, calculation rules, assumptions, exceptions, source inputs, ownership, lineage, tests, and downstream consumers in dbt and Confluence.

- Ensure documentation enables future engineers and analysts to maintain logic without relying on the original analyst.

Gold-Layer Cleanup and Cutover

- Decommission ad hoc Gold-layer logic after the Silver replacement is validated, approved, adopted, and downstream dependencies are updated.

Read from jobs.lever.co, last seen 5d ago.

- Verify retired logic is no longer used and operational documentation reflects the new source of truth.

What they are looking for

- 5–8 years of data engineering experience on analytical data platforms.

- Strong SQL skills: joins, window functions, CTEs, aggregations, conditional logic, and optimisation.

- Hands-on dbt experience across models, tests, documentation, and lineage.

- Strong Snowflake experience, including modelling, performance tuning, and warehouse concepts.

- Experience refactoring, migrating, or modernising complex analytical logic.

- Ability to untangle ad hoc SQL and spreadsheet calculations and validate outputs.

- Strong understanding of data quality, reconciliation, testing, and release controls.

- Skilled at working with non-engineering stakeholders to clarify requirements and validate outcomes.

- Excellent communication and documentation habits.

- SQL; Python a plus

- Snowflake

- dbt, dbt Cloud

- Airflow

- Confluence, Jira, Slack

- Data modelling, quality validation, reconciliation, migration planning, stakeholder collaboration, and technical documentation.