Role Description We at Coforge are hiring a Senior Data Engineer (#21818) with the following skill set. - Design, build, and maintain scalable data pipelines and data platforms using modern cloud technologies. - Develop and optimize ETL/ELT processes, ensuring high data quality, reliability, and performance. - Collaborate with cross-functional teams to support data migration, transformation, and integration initiatives. - Implement best practices for data architecture, governance, automation, and CI/CD processes. Qualifications - 8+ years of experience in Data Engineering. - Strong hands-on experience with Azure Databricks, DBT, and Snowflake. - Advanced SQL skills and expertise in data modeling and ETL/ELT design patterns. - Experience building and maintaining cloud-based data platforms in Azure environments. - Strong understanding of data architecture, scalability, performance optimization, and data integration. - Experience with Infrastructure as Code tools such as Terraform. - Hands-on experience with CI/CD pipelines and Git-based version control. - Experience working on large-scale data migration projects. - Strong analytical, problem-solving, and communication skills. - Experience working in Agile environments and collaborating with business and technical stakeholders. Requirements - Experience with Azure Data Factory, Synapse Analytics, or similar cloud data services. - Knowledge of data governance, data quality, lineage, and observability frameworks. - Experience with streaming technologies and real-time data processing. - Familiarity with lakehouse architectures and modern data platform best practices. - Insurance industry experience is a plus. Company Description At Coforge, we hire professionals based solely on their skills and qualifications and do not discriminate based on age, disability, religion, gender, sexual orientation, socioeconomic status, or nationality.

• Own the gold data layer. Transform messy, silver tables into curated, semantically rich, clean and documented gold datasets suitable for AI model development, including datasets and features reusable for AI development across projects. • Maintain the data as products and needs evolve. To do this you will • Reverse-engineer data semantics. Talk with product engineers, clinical and workflow experts to learn how the products are used and how data are created in the field. • Understand SQL queries, stored procedures, technical data definitions, and other code to know how products represent and transform data. • Learn how data are ingested into the data lake, what silver tables and columns actually represent and how they behave. • Capture provenance, semantics, clinical event sequencing, cross module record linkage and known quirks. • Bridge semantics with AI needs. Understand researcher data needs to design and build the gold data product, with documentation that evolves, to meet AI applied research needs for a highly efficient AI-first foundation for model R&D. • Curate datasets across modalities. For various AI uses such as generative AI, RAG, predictive and other techniques, support researcher needs for chunked and tagged unstructured content with rich metadata, point-in-time-correct features and clean labels. For classical ML and statistical work, deliver model-ready tables. • Build pipelines for reuse. Develop transformations from silver into gold inside Databricks/Spark as scheduled, observable workloads. Design them so researchers can iterate on new features and data mixes without rebuilding from scratch. • Automate quality, filtering, and synthesis. Support research needs for programmatic labeling, weak supervision, near-duplicate detection, boilerplate and noise removal, and LLM-API-driven synthetic data generation where ground truth is scarce. • Version and hand off. Maintain reproducible dataset snapshots. Define clean lineage and semantic definitions so the downstream team can use and re-use gold datasets in AI R&D.

• Toma de requisitos, desarrollo, pruebas funcionales y validación de soluciones de datos. • Diseño e implementación de procesos de carga e integración de datos con herramientas ETL, especialmente PowerCenter, IDQ o DEI. • Promoción a producción y mantenimiento de procesos, asegurando su correcta operativa. • Trabajo con bases de datos y plataformas como BigQuery, Cloudera/Hive y SQL Server, incluyendo documentación técnica.

• Design, build, and optimize robust ETLs using PySpark and DBT to process large-scale customer datasets. • Develop tools and frameworks to streamline data integrations and improve scalability. • Define the technical vision for DC data architecture and mentor engineers. • Manage external contractors to ensure the team delivers high-quality, practical solutions. • Partner with product, engineering, and applied science teams to scope work and deliver data solutions that address real-world challenges in customer data quality and product feature requirements.