IN_Sr Associate_ Data Engineer (Industrial Data Platforms)_AppTech_Advisory_PAN INDIA (Early joiners)

**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:

##

• Understand

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.

• Ingest

and transform time-series, event, master and transactional data using
reusable engineering patterns.

• Contextualize

raw signals against assets, products, batches, operations, shifts and
production orders to create analysis-ready datasets.

• Implement

data-quality rules, lineage, metadata, observability, exception handling and
reconciliation controls.

• Create

curated data models and feature-ready datasets for dashboards, optimization
models, digital twins and machine-learning workloads.

• Develop

secure interfaces and APIs; support cloud, edge and hybrid deployment
patterns where required.

• Write

automated tests, optimize pipeline performance and support release,
deployment and production troubleshooting.

• Maintain

technical specifications, source-to-target mappings, runbooks and handover
documentation.

• Collaborate with

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