Databricks - Sr Technical Lead-Data Engg

Birlasoft; Pune District; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Birlasoft is seeking a Databricks - Sr Technical Lead-Data Engg to architect and build modern data platforms on Azure. You will design broad data pipelines, apply advanced transformations with PySpark, and ensure data quality and governance across domains.

The role focuses on ingestion, orchestration, and analytics-ready data models, collaborating with IT and business teams to deliver scalable, production-grade solutions.

Qualifications

• We are looking for a Databricks Data Engineer with strong hands-on expertise in Databricks, PySpark, ADF, and SQL.
• Experience in architecting, developing, and optimizing modern data platforms on Azure.
• Proven ability to build enterprise-grade data pipelines and scalable analytics solutions.

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.
• Develop robust ELT/ETL pipelines for structured, semi-structured, and unstructured data.
• Integrate data from on-premise and cloud systems, APIs and third-party sources.
• Implement PySpark transformations for performance and modularity.
• Create orchestration workflows in ADF with pipelines and triggers.
• Work with Databricks Genie and dimensional models (star/snowflake).
• Deliver analytics-ready models with consistent KPI definitions.
• Ensure cross-domain consistency and model reusability.

Skills

Databricks

PySpark

ADF

SQL

Delta Lake

Unity Catalog

RBAC/ABAC

ETL/ELT pipelines

Tools

Spark

Azure Data Factory

Job description

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Location: INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI

Title: Databricks - Sr Technical Lead-Data Engg

Description:

Area(s) of responsibility

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.

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.