Sr. Data Engineer – Industry 4.0

Cognizant; City Of London; On-site; GBP 70,000 - 90,000; Full time

Job summary

A global technology company is looking for a Senior Data Engineer to develop scalable data platforms for Industry 4.0. The role includes architecting cloud-native data pipelines, integrating OT and IT systems, and collaborating with AI/ML teams on various initiatives. Ideal candidates will have deep expertise in AWS and/or Azure, programming skills in Python and SQL, and experience with data governance and quality frameworks. This position is based in the City of London, UK.

Qualifications

• Deep experience with AWS and/or Azure cloud platforms.
• Proficiency in programming languages like Python and SQL.
• Expertise in ETL and streaming technologies.

Responsibilities

• Architect and implement cloud-native data pipelines.
• Integrate data from OT and IT systems.
• Design and manage data lakes and warehouses.

Skills

AWS

Azure

Python

SQL

ETL

Data Governance

Data Quality

Tools

Apache Airflow

Kafka

Power BI

Terraform

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.