Databricks Data Engineer

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 modern 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