Data Engineer

Job Responsibilities -

• Architect and implement scalable offline data pipelines for manufacturing systems including AMHS, MES, SCADA, PLCs, vision systems, and sensor data.
• Design and optimize ETL/ELT workflows using Python, Spark, SQL, and orchestration tools (e.g., Airflow) to transform raw data into actionable insights.
• Lead database design and performance tuning across SQL and NoSQL systems, optimizing schema design, queries, and indexing strategies for manufacturing data.
• Enforce robust data governance by implementing data quality checks, lineage tracking, access controls, security measures, and retention policies.
• Optimize storage and processing efficiency through strategic use of formats (Parquet, ORC), compression, partitioning, and indexing for high-performance analytics.
• Implement streaming data solutions (using Kafka/RabbitMQ) to handle real-time data flows and ensure synchronization across control systems.
• Building dashboards using analytics tools like Grafana.
• Good Understanding of Hadoop ecosystem.
• Develop standardized data models and APIs to ensure consistency across manufacturing systems and enable data consumption by downstream applications.
• Collaborate cross-functionally with Platform Engineers, Data Scientists, Automation teams, IT Operations, Manufacturing, and Quality departments.
• Mentor junior engineers while establishing best practices, documentation standards, and fostering a data-driven culture throughout the organization.

Essential Attributes -

• Expertise in Python programming for building robust ETL/ELT pipelines and automating data workflows.
• Proficiency with Hadoops ecosystem.
• Hands-on experience with Apache Spark (PySpark) for distributed data processing and large-scale transformations.
• Strong proficiency in SQL for data extraction, transformation, and performance tuning across structured datasets.
• Proficient in using Apache Airflow to orchestrate and monitor complex data workflows reliably.
• Skilled in real-time data streaming using Kafka or RabbitMQ to handle data from manufacturing control systems.
• Experience with both SQL and NoSQL databases, including PostgreSQL, Timescale DB, and MongoDB, for managing diverse data types.
• In-depth knowledge of data lake architectures and efficient file formats like Parquet and ORC for high-performance analytics.
• Proficient in containerization and CI/CD practices using Docker and Jenkins or GitHub Actions for production-grade deployments.
• Strong understanding of data governance principles, including data quality, lineage tracking, and access control.
• Ability to design and expose RESTful APIs using FastAPI or Flask to enable standardized and scalable data consumption.Qualifications -
• BE/ME Degree in Computer science, Electronics, ElectricalDesired Experience Level -
• Masters+ 2 Years of relevant experience.
• Bachelors+4 Years of relevant experience.
• Experience with semiconductor industry is a plus.