Azure Databricks Specialist

Birlasoft; Pune District; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Birlasoft is seeking a Databricks Data Engineer to architect, develop, and optimize modern data platforms on Azure. You will implement end-to-end data pipelines, ingestion from diverse sources, and scalable analytics solutions. The role requires hands-on expertise in Databricks, PySpark, ADF, and SQL, with a focus on building high-throughput ELT/ETL pipelines and Delta Lake features. You will work with cross-functional teams to deliver production-grade data solutions.

Qualifications

• Minimum 4+ years of data engineering experience in Azure data ecosystem.
• Hands-on Databricks, PySpark, and SQL implementation.
• Experience with Azure Data Factory (ADF) and Delta Lake.
• Strong knowledge of data ingestion, transformation, and quality checks.
• Experience collaborating with cross-functional teams.

Responsibilities

• Data Engineering Execution: build and maintain ingestion frameworks (ADF / Databricks / Spark).
• Build and optimize robust, high-throughput ELT/ETL pipelines for structured, semi-structured, and unstructured data.
• Integrate data from on-premise and cloud systems, APIs, and third-party sources.
• Implement complex transformations using PySpark with performance in mind.
• Build orchestration workflows in ADF with pipelines, triggers, datasets.
• Familiar with Databricks Genie and managing Databricks environments.
• Collaborate with customer IT and business teams to deliver production-ready solutions.
• Mentor junior engineers when required.

Skills

Azure Databricks

PySpark

SQL

Data Engineering

ETL

Cloud data platforms

Tools

ADF / Databricks / Spark

Delta Lake

Unity Catalog

Azure SQL

Job description

Job Description:

Area(s) of responsibility

Skills: Azure Databricks Engineer

Experience: 4–7 Years
Location: Pune, Mumbai, Noida, Chennai, Bangalore

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 Databicks Genie.

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.

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.

Mandatory Skills & Experience

• Minimum 4+ years of experience in data engineering, with strong hands‑on exposure to Azure data ecosystem.


• At least 3 years of real project experience in Databricks (Azure Data Platform (ADF, ADLS, Azure SQL) & Databricks (DLT, Delta Lake, Spark, Workflows, Unity Catalog).


• At least 2 years of hands‑on experience building data pipelines using Azure Data Factory (ADF).


• At least 2 years of experience developing PySpark-based transformations in Databricks.


• Strong SQL programming experience, including writing complex queries, performance tuning, and handling large datasets.

Knowledge of CI/CD pipelines (Azure DevOps preferred) for automated deployment of ADF/Databricks artifacts

Requirements: