Lead Data Engineer #3194

CESIT; Chennai District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

CESIT is seeking a Palantir Foundry Technical Lead specializing in manufacturing data engineering. The role requires strong hands-on experience with Foundry and involves designing scalable data pipelines, defining architecture, and enabling analytical models in manufacturing processes.

The ideal candidate will mentor engineers and ensure data governance and reliability while supporting manufacturing improvements. Candidates should have substantial experience in data engineering, particularly with manufacturing datasets and technical leadership roles.

Qualifications

• Strong hands-on experience with Palantir Foundry.
• Solid data engineering experience with Python and SQL.
• Ability to operate as hands-on engineer and technical lead.

Responsibilities

• Design and implement Foundry pipelines for manufacturing data.
• Define reference architecture for manufacturing data in Palantir Foundry.
• Act as the primary Foundry technical authority.

Skills

Palantir Foundry expertise

Data engineering with Python

Transforms and SQL

Ontology modeling

Manufacturing data experience

Job description

Palantir Foundry Technical Lead – Manufacturing Data Platforms

We are seeking a Palantir Foundry Technical Lead with strong hands‑on experience to support a manufacturing data engineering program. This role combines deep Foundry expertise with manufacturing domain understanding, enabling the design and delivery of scalable, performant, and cost‑optimized data pipelines. The role is intentionally structured as a high‑impact lead engagement, focusing on critical architecture and complex use cases, while enabling the broader engineering team to deliver independently.

Key Responsibilities

1. Hands‑On Foundry Implementation
   • Design and implement Foundry pipelines for manufacturing data such as:
     • Plant, line, and asset data
     • Production volumes and throughput
     • Quality and defect metrics
     • Downtime, OEE, and performance KPIs
   • Develop incremental and near‑real‑time data pipelines where required.
   • Implement ontology models representing:
     • Plants, assets, work centers, products, batches, and orders
   • Handle large‑scale historical and time‑series datasets efficiently.
2. Architecture & Platform Best Practices
   • Define reference architecture for manufacturing data in Palantir Foundry.
   • Establish best practices for pipeline design, orchestration, code repository usage, compute optimization, and cost control.
   • Ensure scalable, maintainable designs that support multi‑plant rollouts.
3. Manufacturing Data Enablement
   • Translate manufacturing processes into analytical data models.
   • Enable use cases such as:
     • Production performance monitoring
     • Root‑cause analysis for quality issues
     • Plant‑to‑plant performance comparison
     • Support for analytics, reporting, and downstream consumption
4. Technical Leadership & Team Enablement
   • Act as the primary Foundry technical authority.
   • Review pipelines, ontology models, and performance‑critical workflows.
   • Mentor data engineers on Foundry and manufacturing data patterns.
   • Enable rapid onboarding of engineers into the Foundry ecosystem.
5. Governance, Security & Reliability
   • Implement data lineage, versioning, and governance in Foundry.
   • Ensure role‑based access aligned with manufacturing and plant‑level controls.
   • Support data quality, validation, and auditability requirements.

Required Skills & Experience

Must Have

• Strong hands‑on experience with Palantir Foundry.
• Expertise in transforms (code & SQL), ontology modeling, code repositories, pipeline orchestration, data lineage, and governance.
• Solid data engineering experience with Python and SQL.
• Experience working with manufacturing datasets.
• Ability to operate as both hands‑on engineer and technical lead.

Good to Have

• Experience with manufacturing systems such as MES, ERP, or plant historians.
• Exposure to time‑series or event‑based manufacturing data.
• Prior experience supporting OEE, quality, or asset performance use cases.
• Experience in advisory or fractional leadership roles.