Birlasoft—Subcon - Data Engineer

Job description

Description: 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.