Databricks And PySpark - Sr Technical Lead-Data Engg

Birlasoft; Pune District; On-site; INR 1,500,000 - 2,700,000; Full time

Job summary

Birlasoft is seeking a Databricks Data Engineer to architect, develop, and optimize modern data platforms on Azure. You will build robust data pipelines, implement Bronze-to-Silver transformations, and ensure data quality across large-scale analytics initiatives with Databricks, PySpark, ADF, and Delta Lake.

Responsibilities include ingestion framework development, governance using Unity Catalog, and collaboration with cross-functional teams to deliver scalable solutions.

Qualifications

• Experience architecting, developing, and optimizing enterprise-grade data platforms on Azure.
• Strong hands-on expertise in Databricks, PySpark, ADF, and SQL.
• Proven ability to build scalable data pipelines ingesting from diverse sources.

Responsibilities

• Data engineering execution: build/maintain ingestion frameworks (ADF/Databricks/Spark).
• Implement Bronze -> Silver transformations and data quality checks.
• Develop scalable ETL pipelines and integrate data from on-premise and cloud systems.
• Create orchestration workflows in ADF and manage Databricks clusters, jobs, notebooks.
• Ensure data governance via Unity Catalog, RBAC/ABAC, and metadata policies.
• Collaborate with IT and business teams to deliver production-ready solutions.

Skills

Data pipeline design

Stakeholder collaboration

Data governance

Tools

Databricks

PySpark

Azure Data Factory (ADF)

SQL

Delta Lake

Unity Catalog

Job description

Job Description:

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

1. 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 Databricks Genie.
• 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

2. Databricks & PySpark Engineering

• Develop scalable transformation scripts using PySpark on Databricks, applying advanced optimizations like caching, partitioning, and Delta Lake capabilities.
• Implement Delta Lake features—ACID transactions, schema enforcement, schema evolution, and time travel—across 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.

3. 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 and validate metrics and edge cases.

4. 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