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Senior Software Engineer, Data Engineering

ValGenesis
Chennai, IND
Full-time
Senior · 4+ years experience
Onsite
Discovered 3 weeks ago
PythonSQLC#JavaScalaAzure Data Lake
Free

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  • Design, develop, and maintain data ingestion, transformation, and orchestration pipelines (batch and real-time).
  • Build and optimize data Lakehouse architectures using Azure Synapse, Delta Lake, or similar frameworks.
  • Integrate and manage structured and unstructured data sources (SQL/NoSQL, files, documents, IoT streams).
  • Develop and operationalize ETL/ELT pipelines using Azure Data Factory, Databricks, or Apache Spark.
  • Collaborate with Data Scientists to prepare and serve ML-ready datasets for model training and inference.
  • Implement data quality, lineage, and governance frameworks across pipelines and storage layers.
  • Work with BI tools (Power BI, Superset, Tableau) to enable self-service analytics for business teams.
  • Deploy and maintain data APIs and ML models in production using Azure ML, Kubernetes, and CI/CD pipelines.
  • Ensure scalability, performance, and observability of data workflows through effective monitoring and automation.
  • Collaborate cross-functionally with engineering, product, and business teams to translate insights into action.
  • Experience: 4 to 8 years in Data Engineering or equivalent roles.
  • Programming: Strong in Python, SQL, and at least one compiled language (C#, Java, or Scala).
  • Databases: Experience with relational (SQL Server, PostgreSQL, MySQL) and NoSQL (MongoDB, Cosmos DB) systems.
  • Data Platforms: Hands-on experience with Azure Data Lake, Databricks.
  • ETL/ELT Tools: Azure Data Factory, Apache Airflow, or dbt .
  • Messaging & Streaming: Kafka, Event Hubs, or Service Bus for real-time data processing.
  • AI/ML Exposure: Familiarity with ML frameworks (TensorFlow, PyTorch ) and MLOps concepts.
  • Visualization & Analytics: Power BI, Apache Superset, or Tableau.
  • Cloud & DevOps: Azure, Docker, Kubernetes, GitHub Actions/Azure DevOps.
  • Best Practices: Solid understanding of data modeling, version control, and CI/CD for data systems.
  • Experience in knowledge graph or semantic search solutions.
  • Understanding of LLM-based data retrieval (RAG) patterns.
  • Exposure to data mesh, data fabric, or domain-oriented data architecture.
  • Familiarity with MLflow , Delta Live Tables, or DataBricks Unity Catalog.
  • Strong analytical and problem-solving ability.
  • Excellent communication and collaboration skills.
  • Attention to detail and ability to work with large, complex datasets.
  • Creativity and ability to automate repetitive workflows.
  • Passion for continuous learning and innovation in data and AI technologies.

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