We are looking for a Senior Data Engineer with strong expertise in Azure Databricks, PySpark, and distributed computing to develop and optimize scalable ETL pipelines for manufacturing analytics. The role involves working with high-frequency industrial data to enable real-time and batch data processing.

Key Responsibilities :

– Build scalable real-time and batch processing workflows using Azure Databricks, PySpark, and Apache Spark.

– Perform data pre-processing, including cleaning, transformation, deduplication, normalization, encoding, and scaling to ensure high-quality input for downstream analytics.

– Design and maintain cloud-based data architectures, including data lakes, lakehouses, and warehouses, following Medallion Architecture.

– Deploy and optimize data solutions on Azure (preferred), AWS, or GCP with a focus on performance, security, and scalability.

– Develop and optimize ETL/ELT pipelines for structured and unstructured data from IoT, MES, SCADA, LIMS, and ERP systems.

– Automate data workflows using CI/CD and DevOps best practices, ensuring security and compliance with industry standards

– Monitor, troubleshoot, and enhance data pipelines for high availability and reliability.

– Utilize Docker and Kubernetes for scalable data processing.

– Collaborate with automation team, data scientists and engineers to provide clean, structured data for AI/ML models.

Desired Skills and Qualifications :

– Bachelors or Masters degree in Computer Science, Information Technology, or a related field from IIT/NIT/BITS/IIIT

– 7+ years of experience in core data engineering, with a strong focus on cloud platforms such as Azure (preferred), AWS, or GCP

– Proficiency in PySpark, Azure Databricks, Python and Apache Spark, etc.

– 2 years of team handling experience.

– Expertise in relational databases (e.g., SQL Server, PostgreSQL), time series databases (e.g. Influx DB), and NoSQL databases (e.g., MongoDB, Cassandra)

– Experience in containerization (Docker, Kubernetes).

– Strong analytical and problem-solving skills with attention to detail.

– Good to have MLOps, DevOps including model lifecycle management

– Excellent communication and collaboration skills, with a proven ability to work effectively as a team player.

– Comfortable working in a dynamic, fast-paced startup environment, adapting quickly to changing priorities and responsibilities.