Data Engineer

Brillio

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

Posted 10/9/2026full-timeBangalore • IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in SQL, dbt, and Snowflake for data engineering, focusing on building scalable and maintainable data models while ensuring data quality and effective stakeholder communication.

Highest-signal resume keywords

Strong SQL SkillsHands-On Dbt ExperienceStrong Snowflake ExperienceData Quality UnderstandingTechnical Documentation Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

SQLDbtSnowflakeData ModellingData QualityReconciliationMigration PlanningAnalytical Logic RefactoringPerformance TuningConditional Logic

Soft Skills

Excellent CommunicationStakeholder CollaborationDocumentation Habits

Tools & Technologies

AirflowConfluenceJiraSlack

Industry Keywords

Data EngineeringAnalytical Data PlatformsVersion ControlCode Review StandardsBusiness Rules

Tech Stack

Tools & technologies

AirflowPythonSQL

About the role

Key responsibilities & impact

Work with pricing analysts to identify custom definitions, calculations, transformations, dependencies, assumptions, edge cases, and ambiguities 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
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
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 after parity review and pricing analyst approval
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
Document business meaning, calculation rules, assumptions, exceptions, source inputs, ownership, lineage, tests, and downstream consumers in dbt and Confluence
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

Requirements

What you’ll need

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
Python a plus
Experience with Airflow, Confluence, Jira, and Slack
Experience with data modelling, quality validation, reconciliation, migration planning, stakeholder collaboration, and technical documentation