Role & responsibilities seeking a Senior Data Engineer who thinks beyond one-off ETL development. The successful candidate will build dbt transformation projects, configurable transformation patterns, canonical models, semantic assets, database solutions, and data foundations that can be reused across customer environments while allowing customer-specific differences through metadata, mappings, parameters, and governed extension points.

Preferred candidate profile

Required dbt experience

Hands-on production experience with dbt Core or dbt Cloud is required. Candidates without direct dbt implementation experience should not be advanced.

What You Will Do

- Design and implement reusable data products, canonical models, and semantic layers for the products and client delivery.
- Build and maintain dbt Core or dbt Cloud projects with staging, intermediate, and mart layers, reusable macros, tests, documentation, and lineage.
- Develop production-grade database objects and transformation logic across multiple modern data platforms.
- Create configuration-driven and metadata-driven transformation patterns, mappings, and business rules.
- Design dimensional models, conformed dimensions, facts, SCD Type 2 history, and cross-domain structures.
- Contribute to Customer 360, MDM, identity resolution, source precedence, survivorship, and relationship-layer patterns.
- Build data-quality tests, reconciliation controls, documentation, deployment automation, and operational monitoring.
- Collaborate with AI engineers and architects so governed data and semantic assets can support conversational and agentic experiences.

Required Qualifications





- 8+ years of data engineering and database development experience with advanced SQL and strong Python skills.
- Hands-on production experience with dbt Core or dbt Cloud, including direct development of models, tests, documentation, macros or reusable patterns, and deployment workflows.
- Hands-on database development experience across multiple platforms such as Azure SQL, Snowflake, Databricks, Amazon Redshift, or comparable enterprise data platforms.
- Experience building reusable ETL/ELT frameworks using metadata, configuration, or parameter-driven logic.
- Solid dimensional-modeling experience, including grain, conformed dimensions, facts, and SCD Type 2.
- Experience with semantic layers, governed metrics, reusable business models, Customer 360, MDM, or identity-resolution problems.
- Experience with automated data testing, Git, CI/CD, documentation, and production support.
- Ability to convert ambiguous or changing business rules into maintainable, testable data products.

Preferred Qualifications

- Azure data services and cloud-native data architecture
- Spark, Delta Lake, and/or Unity Catalog
- Metric stores or semantic-layer frameworks
- YAML/JSON metadata design
- Entity matching, survivorship, and golden-record design
- Gaming, Hospitality, Customer 360, loyalty, or cross-domain analytics experience

Success in the First Six Months

- Deliver reusable dbt transformation assets with tests and documentation.
- Establish or improve canonical-to-customer mapping and configuration patterns.
- Deliver reliable database development across the products supported data platforms.
- Advance Customer 360, Property 360, and cross-domain semantic structures.
- Reduce customer-specific code by moving variable logic into governed configuration and metadata.

📌 Senior Data Engineer (Hyderabad)
🏢 InfoMagnus
📍 Hyderabad