Senior/Lead Data Engineer

You are a Senior Data Engineer who will be responsible for developing and optimizing scalable ETL pipelines for manufacturing analytics using Azure Databricks, PySpark, and distributed computing. Your role will involve 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 tasks such as 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 the 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 Tier 1 institutes (IIT, NIT, IIIT, DTU, etc.). - 5+ 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. - 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. You are a Senior Data Engineer who will be responsible for developing and optimizing scalable ETL pipelines for manufacturing analytics using Azure Databricks, PySpark, and distributed computing. Your role will involve 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 tasks such as 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 the 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 Tier 1 institutes (IIT, NIT, IIIT, DTU, etc.). - 5+ 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. - 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.