{"id":3798804,"url":"https://alion.io/job/primesoft-solutions-senior-data-engineer-azure-databricks","title":"Senior Data Engineer - Azure Databricks","company":{"id":3806476,"name":"PrimeSoft Solutions","domain":"primesoft.net","url":"https://alion.io/company/primesoft-solutions","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Darwinbox","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure Data Factory","optional":false},{"name":"Azure SQL Database","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Feature Store","optional":false},{"name":"Machine Learning","optional":false},{"name":"Microsoft Fabric","optional":false},{"name":"MS SQL","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-11-05T08:40:49Z","employer_posted_date":null,"last_verified_at":"2026-11-05T08:40:49Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Senior Data Engineer - Azure Databricks & Microsoft Fabric\n\nRole Overview:\n\nWe are looking for an experienced Senior Data Engineer with strong hands-on expertise in Python, PySpark, SQL, Azure Databricks, and Microsoft Fabric. The candidate will be responsible for designing and maintaining scalable batch and real-time data pipelines, developing lakehouse architectures, and enabling reliable data solutions for analytics and AI/ML applications.\n\nKey Responsibilities:\n\nData Pipeline & Lakehouse Engineering:\n\n- Design, implement, and maintain high-performance batch and real-time streaming data pipelines using Python, PySpark, and Spark SQL. Build multi-tier Medallion architectures (Bronze, Silver, and Gold) using Delta Lake principles.\n\nData Platform Delivery:\n\n- Leverage OneLake, Direct Lake, Lakehouses, and Data Factory Gen2 to streamline data ingestion, optimise semantic models, and reduce redundant ETL processes.\n\nAPI & Systems Integration:\n\n- Build resilient API connectors, Change Data Capture (CDC) workflows, and data integration pipelines to ingest data from multiple heterogeneous sources.\n\nAI & Analytics Enablement:\n\n- Structure, clean, and optimise lakehouse data layers for Power BI reporting and AI/ML applications, ensuring data is validated and structured for AI grounding.\n\nData Quality & Governance:\n\n- Implement data validation, access controls, data lineage, and metadata management using Unity Catalog, platform governance capabilities, and automated testing frameworks.\n\nTechnical Mentorship & CI/CD:\n\n- Conduct code reviews, enforce engineering best practices, optimise query performance, and manage automated deployment pipelines.\n\nRequired Technical Skills:\n\n- Advanced hands-on proficiency in Python, PySpark, and SQL, including Spark SQL and T-SQL.\n\n- Strong experience with Azure Databricks, including Delta Lake, Unity Catalog, Delta Live Tables, Auto Loader, and Genie.\n\n- Demonstrated experience developing data pipelines and workspace components within Microsoft Fabric.\n\n- Expertise in Azure cloud services, including ADLS Gen2, Azure Data Factory (ADF), Azure Key Vault, and Azure SQL / Azure Synapse.\n\n- Strong understanding of data engineering requirements for AI/ML workloads, including knowledge of vector embeddings, RAG data ingestion, and feature stores.\n\nEducation:\n\n- Bachelor's or Master's degree in Computer Science, Information Technology, or a related quantitative field.\n\nSkills\nData Engineering, Azure Databricks, Microsoft Fabric, Python, PySpark, SQL, Delta Lake, Azure Data Factory, CI/CD, RAG, Machine 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