About the job:We're hiring a Data Engineer to build and maintain governed, BI-ready data layers in Databricks across real client use cases. You'll work across Databricks, SQL, PySpark, Delta Lake, and contemporary data engineering infrastructure to turn functional requirements and source-system logic into scalable, reliable data models. This is a contract, fully remote role for an India-based engineer who is comfortable working hands-on with data pipelines, dimensional models, validation, and production-grade data systems.

About us:We're a new age tech & business services company. We're HQ'ed in Hyderabad with clients across the world. Find out more about us at https://www.linkedin.com/company/gitforce

What You'll DoTL;DR: Build governed, BI-ready data pipelines, models, and metric layers in Databricks using SQL, PySpark, Delta Lake, and Unity Catalog.

- Build and test data pipelines, derived tables, dimensional models, and BI-ready data layers in Databricks
- Design and maintain Unity Catalog structures, catalogs, schemas, permissions, and access controls
- Translate functional requirements and PostgreSQL source logic into scalable data transformations
- Design fact and dimension models and Gold-layer datasets optimized for reporting and analytics
- Build Databricks metric views including dimensions, measures, joins, filters, and reusable business metrics
- Develop data transformations using SQL, PySpark, Python, Spark, and Delta Lake
- Implement source-to-target reconciliation and ensure transformed data accurately reflects source systems
- Build data-quality checks, validation rules, pipeline tests, and reconciliation frameworks




- Design data models that support multi-tenant SaaS applications and appropriate tenant-level data separation
- Optimize Databricks pipelines, SQL queries, storage layouts, and workloads for performance and scalability
- Follow Git-based development workflows and CI/CD practices for data engineering
- Work closely with functional, validation, BI, and engineering teams to translate business requirements into reliable data solutions
- Debug data issues, investigate discrepancies, and improve the reliability of production data pipelines
- Work under the direction of a technical lead while independently owning assigned pipelines, models, and transformations

QualificationsTL;DR: Strong Databricks, SQL, PySpark, Delta Lake, dimensional modelling, and data validation experience are the most important things we're looking for.

You Are:

- 2 to 5 years into your data engineering or software engineering career as a professional
- Someone who enjoys working hands-on with data pipelines, transformations, and analytical data models
- Comfortable taking functional requirements and translating them into practical technical implementations
- An engineer who cares about data correctness, maintainability, performance, and production reliability
- Comfortable working independently while collaborating closely with technical leads,



functional teams, validation teams, and BI teams

Must-haves:

- Strong hands-on experience with Databricks
- Strong SQL programming and data transformation skills
- Experience with PySpark, Python, Spark, and Delta Lake
- Experience designing and maintaining Unity Catalog structures, schemas, permissions, and access controls
- Strong understanding of dimensional modelling, including fact and dimension tables
- Experience building BI-ready Gold-layer data models
- Experience working with Databricks metric views including dimensions, measures, joins, and filters
- Hands-on experience with data validation, source-to-target reconciliation, data-quality checks, and pipeline testing
- Experience translating source-system logic into scalable data transformations
- Experience designing data models for multi-tenant SaaS applications
- Experience with Git-based development workflows and CI/CD practices
- Good understanding of data engineering architecture, testing, performance optimization, and production engineering fundamentals
- Strong problem-solving and communication skills

Nice-to-haves:

- Experience working with PostgreSQL source systems
- Experience building incremental data pipelines or CDC-based ingestion pipelines
- Experience with Lakeflow Declarative Pipelines and data-quality expectations
- Experience with Databricks Asset Bundles
- Experience with dbt
- Experience optimizing Databricks SQL queries and workloads
- Experience designing reusable semantic or metrics layers for BI applications
- Experience working in complex SaaS or enterprise data environments

📌 Databricks Data Engineer (India)
🏢 Gitforce
📍 India