Databricks - Technical Specialist-Data Engg

Birlasoft; Pune District; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

Birlasoft is seeking a highly skilled Databricks Data Engineer to architect, develop, and optimize modern data platforms on Azure. You will build enterprise-grade data pipelines, enable ingestion from diverse sources, and implement complex transformations to support scalable analytics initiatives.

The role requires strong hands-on experience with Databricks, PySpark, ADF, SQL, and data governance. You will collaborate with cross-functional teams to deliver production-ready data solutions and

Qualifications

• Experience building enterprise-grade data pipelines on Azure.
• Strong hands-on expertise with Databricks, PySpark, ADF, and SQL.
• Ability to ingest data from diverse sources and implement complex transformations.

Responsibilities

• Build and maintain ingestion frameworks (ADF / Databricks / Spark).
• Implement Bronze -> Silver transformations aligned to architecture.
• Architect data quality checks, schema validation, and contract rules.
• Build and optimize robust, high-throughput ELT/ETL pipelines for structured, semi-structured, and unstructured data.
• Integrate data from multiple on-premise and cloud systems, APIs, and third-party sources.
• Implement complex PySpark transformations for performance and modularity.
• Create orchestration workflows in ADF with pipelines, triggers, and datasets.
• Familiar with Databricks Genie for workflow automation.
• Design curated data models in the Gold layer aligned to business use cases.
• Develop Dimensional models (star/snowflake) and surrogate keys.
• Translate Silver datasets into analytics-ready models.
• Define consistent KPI definitions and business logic across domains.
• Ensure data consistency and reusability of models.

Skills

Databricks

PySpark

Azure Data Factory

SQL

Tools

Azure Databricks

Delta Lake

Unity Catalog

Databricks Genie

Job description

Area(s) of responsibility

Role Summary

We are looking for a highly skilled Databricks Data Engineer with strong hands on expertise in Databricks, PySpark, ADF, and SQL. The candidate will be responsible for architecting, developing, and optimizing modern data platforms and analytical solutions on Azure. The role requires strong experience building enterprise-grade data pipelines, enabling ingestion from diverse sources, implementing complex transformations, and supporting scalable analytics initiatives.

Key Responsibilities

Data Engineering Execution

• Build and maintain ingestion frameworks (ADF / Databricks / Spark)
• Implement Bronze -> Silver transformations aligned to architecture
• Architect data quality checks, schema validation, and contract rules
• Build and optimize robust, high-throughput ELT/ETL pipelines, enabling ingestion, transformation, and curation of structured, semi structured, and unstructured data.
• Integrate data from multiple on premise and cloud based systems, APIs, and third-party sources.
• Implement complex transformations using PySpark, ensuring performance efficiency and code modularity.
• Build orchestration workflows in ADF, including pipelines, triggers, linked services, integration runtimes, and parameterized datasets.
• Familiar with using Databicks Genie.
• Design and implement curated data models in Gold layer aligned to business use cases.
• Build Dimensional models (star/snowflake schemas)
• Fact tables, dimensions, surrogate keys, SCD handling
• Translate Silver Datasets Into Analytics-ready models
• Consistent KPI definitions and business logic
• Ensure Consistency across domains (common dimensions, conformed models)
• Reusability and scalability of models
• Databricks & PySpark Engineering
• Develop scalable transformation scripts using PySpark on Databricks, applying advanced optimizations like caching, partitioning, and Delta Lake capabilities.
• Implement Delta Lake featuresACID transactions, schema enforcement, schema evolution, and time travelacross the data lifecycle.
• Perform performance tuning, handling bottlenecks related to cluster configuration, shuffle operations, joins, and parallelization.
• Collaborate with platform teams to manage Databricks clusters, jobs, notebooks, and CI/CD integrations.

Data Governance & Quality

• Implement data quality checks, audit mechanisms, and validation frameworks to ensure data accuracy and consistency in Unity Catalog.
• Enforce Unity Catalog standards naming conventions, metadata policies access controls (RBAC/ABAC)
• Handle changes such as:
  • Column derivation logic changes (not just schema changes)
  • Backward compatibility and impact analysis
• Partner with business / data stewards to:
  • Define business rules
  • Validate metrics and edge cases

Collaboration & Stakeholder Management

• Collaborate closely with customer IT and business teams to understand data requirements and deliver reliable, production-ready solutions.
• Work with architects, product owners, and cross-functional engineering teams to align technical delivery with business objectives.
• Provide guidance and mentoring to junior engineers when required.