Job Description

JD: Sr. Data Engineer - Industry 4.0

We are hiring a senior Data Engineer to lead the development of intelligent, scalable data platforms for Industry 4.0 initiatives. This role will drive integration across OT/IT systems, enable real-time analytics, and ensure robust data governance and quality frameworks. The engineer will collaborate with cross-functional teams to support AI/ML, GenAI, and IIoT use cases in manufacturing and industrial environments.

Key Responsibilities

Architect and implement cloud-native data pipelines on AWS or Azure for ingesting, transforming, and storing industrial data. Integrate data coming from OT systems (SCADA, PLC, MES, Historian) and IT systems (ERP, CRM, LIMS) using protocols like OPC UA, MQTT, REST. Design and manage data lakes, warehouses, and streaming platforms for predictive analytics, digital twins, and operational intelligence. Define and maintain asset hierarchies, semantic models, and metadata frameworks for contextualized industrial data. Implement CI/CD pipelines for data workflows and ensure lineage, observability, and compliance across environments. Collaborate with AI/ML teams to support model training, deployment, and monitoring using MLOps frameworks. Establish and enforce data governance policies, stewardship models, and metadata management practices Monitor and improve data quality using rule-based profiling, anomaly detection, and GenAI-powered automation Support GenAI initiatives through data readiness, synthetic data generation, and prompt engineering.

Mandatory Skills:

Cloud Platforms: Deep experience with AWS (S3, Lambda, Glue, Redshift) and/or Azure (Data Lake, Synapse). Programming & Scripting: Proficiency in Python, SQL, PySpark etc. ETL/ELT & Streaming: Expertise in technologies like Apache Airflow, Glue, Kafka, Informatica, EventBridge etc. Industrial Data Integration: Familiarity with OT data schema originating from OSIsoft PI, SCADA, MES, and Historian systems. Information Modeling: Experience in defining semantic layers, asset hierarchies, and contextual models. Data Governance: Hands-on experience Data Quality: Ability to implement profiling, cleansing, standardization, and anomaly detection frameworks. Security & Compliance: Knowledge of data privacy, access control, and secure data exchange protocols. Defining and creating MLOPs pipeline

Good to Have Skills

GenAI Exposure: Experience with LLMs, LangChain, HuggingFace, synthetic data generation, and prompt engineering. Digital Twin Integration: Familiarity with nVidia Omniverse, AWS TwinMaker, Azure Digital Twin or similar platforms and concepts Visualization Tools: Power BI, Grafana, or custom dashboards for operational insights. DevOps & Automation: CI/CD tools (Jenkins, GitHub Actions), infrastructure-as-code (Terraform, CloudFormation). * Industry Standards: ISA-95, Unified Namespace (UNS), FAIR data principles, and DataOps methodologies.