Lead Software Engineer, Data Engineering
Team: Engineering
Location: Chennai
Commitment: Full-Time
Workplace Type: onsite
Responsibilities
- Architect, 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.
- Mentor junior team members and review their work
Requirements
- Experience: 6 to 12 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.
Preferred skills
- Preferred Skills (Nice to Have):
- 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 Data Bricks Unity Catalog.
Soft skills
- 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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