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Data Engineer - R01571442

📅 Sep 22
📍 🇮🇳 Bengaluru - Hybrid🎹 Mid-level🛢️ Data Engineer💼 Full-Time
SQLPythonSnowflakeDbt
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📜 Description

  • Work with pricing analysts to uncover and clarify business logic embedded in Gold-layer SQL, spreadsheets, and ad hoc calculations.
  • Translate legacy logic into technical requirements and migration plans, then implement reusable dbt models in the Silver layer.
  • Apply data modelling, testing, version control, and code review standards to build maintainable, auditable models.
  • Validate migrated results against Gold-layer outputs across periods, segments, edge cases, and exceptions; resolve differences and obtain analyst approval.
  • Lead analyst walkthroughs, communicate risks and decisions, and document business rules, assumptions, lineage, tests, and downstream consumers.
  • Retire legacy Gold-layer logic after validation and approval, update dependencies, and confirm the new source of truth is adopted.

🛠️ Requirements

  • 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.
  • 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.
Full job description
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.
    • Apply Now

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