Job Description & Summary
We are looking for a hands-on Data Engineer to build reliable, contextualized and AI-ready industrial data foundations. The role connects operational technology and enterprise information technology data, develops scalable pipelines, and ensures that manufacturing data can be trusted and reused across analytics, digital twin and AI solutions.
A career within Enterprise Architecture services, will provide you with the opportunity to bring our clients a competitive advantage through defining their technology objectives, assessing solution options, and devising architectural solutions that help them achieve both strategic goals and meet operational requirements. We help build software and design data platforms, manage large volumes of client data, develop compliance procedures for data management, and continually researching new technologies to drive innovation and sustainable change.
Job Position Title
IN_Sr Associate_ Data Engineer (Industrial Data Platforms)_AppTech_Advisory_PAN INDIA (Early joiners)
Responsibilities
manufacturing processes, equipment hierarchies, tags, events and business data requirements with plant and solution teams.
- Design, build and maintain batch
and streaming pipelines for historian, IoT, PLC/SCADA, MES, ERP, quality, maintenance and laboratory data.
and transform time-series, event, master and transactional data using reusable engineering patterns.
raw signals against assets, products, batches, operations, shifts and production orders to create analysis-ready datasets.
data-quality rules, lineage, metadata, observability, exception handling and reconciliation controls.
curated data models and feature-ready datasets for dashboards, optimization models, digital twins and machine-learning workloads.
secure interfaces and APIs; support cloud, edge and hybrid deployment patterns where required.
automated tests, optimize pipeline performance and support release, deployment and production troubleshooting.
technical specifications, source-to-target mappings, runbooks and handover documentation.
data scientists, UX engineers, architects and manufacturing subject-matter experts throughout delivery.
Expected Delivery Outcomes
Stable and monitored pipelines with agreed service levels and recoverability.
Traceable, quality-assured and reusable industrial datasets.
Clear technical documentation and effective handover to operations/support teams.
Working Style and Stakeholder Engagement
Work as part of a cross-functional delivery POD with manufacturing, engineering, data, cloud, enterprise application and change teams.
Communicate complex technical topics in clear business language and maintain disciplined documentation, risk tracking and knowledge transfer.
Support client workshops, design reviews, testing, deployment and adoption activities; travel to manufacturing locations when required.
Mandatory skill sets
4-8 years of experience in data engineering, analytics engineering or industrial data integration.
Strong SQL and Python skills, with practical experience building production-grade extract, transform and load pipelines.
Experience with cloud data platforms, data lakes/lakehouses, orchestration frameworks and version-controlled engineering practices.
Working knowledge of streaming and time-series data, message brokers, APIs and industrial connectivity patterns.
Understanding of manufacturing data sources such as MES, ERP, historian, IoT, PLC/SCADA, quality and maintenance systems.
Experience with data modelling, data-quality controls, monitoring, security and access management.
Ability to investigate data issues independently and communicate findings to technical and business stakeholders.
Bachelor’s degree in engineering, computer science, information technology or a related discipline
Preferred skill sets
Experience with Databricks, Microsoft Fabric, Azure Data Factory, Spark, Kafka/Event Hubs or equivalent technologies.
Knowledge of ISA-95, OPC UA, MQTT, asset models, knowledge graphs or digital-twin data models.
Exposure to MLOps feature stores, containerization and continuous integration/continuous delivery.
Experience delivering data products in automotive, metals, chemicals, consumer products or industrial manufacturing
Years of experience required:
4-8 years
Education qualification
B.E./ B.Tech / MCA/ M.E/ M.TECH/ MBA/ 2 yrs PGDM.. All qualifications should be in regular full-time mode with no extension of course duration due to backlogs
