{"id":124441,"url":"https://alion.io/job/valgenesis-senior-software-engineer-data-engineering","title":"Senior Software Engineer, Data Engineering","company":{"id":6387,"name":"ValGenesis","domain":"valgenesis.com","url":"https://alion.io/company/valgenesis","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":93,"open_postings":9,"ghost_share":0,"stale_share":0,"repost_share":0.111,"time_to_fill_p50_days":394,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, 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Search","optional":true}],"status":"live","first_seen_at":"2026-04-24T06:16:34Z","employer_posted_date":"2026-04-24","last_verified_at":"2026-10-17T02:32:18Z","board_verified":true,"closed_at":null,"days_open":175,"trust":{"level":"ok","repost_count":0,"flags":[],"days_open":174},"description":"Responsibilities:\nDesign, develop, and maintain data ingestion, transformation, and orchestration pipelines (batch and real-time).\n\nBuild and optimize data Lakehouse architectures using Azure Synapse, Delta Lake, or similar frameworks.\n\nIntegrate and manage structured and unstructured data sources (SQL/NoSQL, files, documents, IoT streams).\n\nDevelop and operationalize ETL/ELT pipelines using Azure Data Factory, Databricks, or Apache Spark.\n\nCollaborate with Data Scientists to prepare and serve ML-ready datasets for model training and inference.\n\nImplement data quality, lineage, and governance frameworks across pipelines and storage layers.\n\nWork with BI tools (Power BI, Superset, Tableau) to enable self-service analytics for business teams.\n\nDeploy and maintain data APIs and ML models in production using Azure ML, Kubernetes, and CI/CD pipelines.\n\nEnsure scalability, performance, and observability of data workflows through effective monitoring and automation.\n\nCollaborate cross-functionally with engineering, product, and business teams to translate insights into action.\n\nRequirements:\nExperience: 4 to 8years in Data Engineering or equivalent roles.\n\nProgramming: Strong in Python, SQL, and at least one compiled language (C#, Java, or Scala).\n\nDatabases: Experience with relational (SQL Server, PostgreSQL, MySQL) and NoSQL (MongoDB, Cosmos DB) systems.\n\nData Platforms: Hands-on experience with Azure Data Lake, Databricks.\n\nETL/ELT Tools: Azure Data Factory, Apache Airflow, or dbt.\n\nMessaging & Streaming: Kafka, Event Hubs, or Service Bus for real-time data processing.\n\nAI/ML Exposure: Familiarity with ML frameworks (TensorFlow, PyTorch) and MLOpsconcepts.\n\nVisualization & Analytics: Power BI, Apache Superset, or Tableau.\n\nCloud & DevOps: Azure, Docker, Kubernetes, GitHub Actions/Azure DevOps.\n\nBest Practices: Solid understanding of data modeling, version control, and CI/CD for data systems.\n\nPreferred Skills (Nice to Have):\nExperience in knowledge graph or semantic search solutions.\n\nUnderstanding of LLM-based data retrieval (RAG) patterns.\n\nExposure to data mesh, data fabric, or domain-oriented data architecture.\n\nFamiliarity with MLflow, Delta Live Tables, or DataBricksUnity Catalog.\n\nSoft Skills:\nStrong analytical and problem-solving ability.\n\nExcellent communication and collaboration skills.\n\nAttention to detail and ability to work with large, complex datasets.\n\nCreativity and ability to automate repetitive workflows.\n\nPassion for continuous learning and innovation in data and AI 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