Geared toward candidates comfortable with reverse-engineering complex analytical logic and collaborating closely with pricing analysts, this role requires strong technical expertise in SQL, dbt, and Snowflake, with a focus on documentation and lineage in a Bangalore-based setting.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Brillio 2 via Lever
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.
- Verify retired logic is no longer used and operational documentation reflects the new source of truth.Required Qualifications
- 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.Core Skills and Tools
- SQL; Python a plus
- Snowflake
- dbt, dbt Cloud
- Airflow
- Confluence, Jira, Slack
- Data modelling, quality validation, reconciliation, migration planning, stakeholder collaboration, and technical documentation.
Source: Brillio 2 careers (Lever)