Subcon - Data Engineer

Birlasoft•INDIA - PUNE - BIRLASOFT OFFICE - HINJAWADI, IN•On-site
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About the Role

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. <div class="