Subcon - Data Engineer

Area(s) of responsibility

mpowered By Innovation

Birlasoft, a global leader at the forefront of Cloud, AI, and Digital technologies, seamlessly blends domain expertise with enterprise solutions. The company’s consultative and design-thinking approach empowers societies worldwide, enhancing the efficiency and productivity of businesses. As part of the multibillion-dollar diversified CKA Birla Group, Birlasoft with its 12,000+ professionals, is committed to continuing the Group’s 170-year heritage of building sustainable communities.

Job Title / Position: Data Engineer (contractual) Experience: 6-8 years

Location: Pune

The Data Engineer designs, develops, and manages scalable data platforms, pipelines, and data products that provide trusted, governed, and high-quality data for analytics, AI/ML, reporting, and business operations. The role enables enterprise-wide data availability while ensuring reliability, security, scalability, and compliance.

Key Responsibilities

• Design and develop data ingestion, transformation, and integration pipelines.
• Build scalable batch, streaming, and near real-time data solutions.
• Develop and optimize data lake, lakehouse, and data warehouse architectures.
• Create reusable data models, curated datasets, and data products.
• Implement data quality, governance, lineage, monitoring, and security controls.
• Collaborate with architects, analysts, data scientists, and business teams to deliver data solutions.
• Troubleshoot production issues and improve operational stability.
• Drive automation, standardization, and adoption of modern data engineering practices.

Core Skills & Technologies

• SQL, Python, PySpark, Scala, Java
• Databricks, Delta Lake
• Azure Data Factory, Synapse, ADLS
• Snowflake and Cloud Data Platforms
• Data Modeling (Star Schema, Snowflake Schema, Data Vault)
• CI/CD, DevOps, Monitoring & Observability
• Data Governance, Metadata & Lineage

Success Measures

• Reliable, secure, and governed enterprise data delivery
• Improved scalability and performance of data platforms
• Reduced manual effort through automation
• High adoption of data products across analytics and business teams
• Enablement of business use cases including manufacturing analytics, pricing analytics, prognostics, and commercial operations.