Senior Data Engineer - Integration Lead, Supply Chain

Cognite Cognite · Industrial · India · Center of Excellence

Senior Data Engineer responsible for designing and building data pipelines to power industrial GenAI solutions in the supply chain domain. This involves integrating data from various sources (SAP, external platforms), optimizing it for a knowledge graph, and ensuring it's ready for consumption by AI agents.

What you'd actually do

  1. Architect Standard Integrations: Design, implement and package scalable data pipelines that ingest and normalize data from external logistics aggregators (& syndicate data sources) and internal systems (ERP/MES).
  2. Data Model Optimization: Enhance and refine the Integrated Supply Chain Solution Model. You will be responsible for defining and packaging the relationships between disparate data points from source systems —such as linking a real-time shipment tracking data from a third-party logistics service provider or a PurchaseOrderLineItem.
  3. Knowledge Graph Engineering: Build and enhance the graph structures within CDF that allow AI agents to navigate from a Notification (e.g., a delayed ship) to the impacted ProductionSchedule or WorkOrder.
  4. Advanced DataOps: Lead extractions, data modeling, semantic ontology and build high performant ETL using Python, SQL, and REST APIs, ensuring that industrial data types (time series and contextual events) are "agent-ready". Develop transforms that adapt to data type and frequency, aligned with use case requirements.
  5. Technical Consultation: Act as a domain expert for customers, guiding them on how to map their complex supply chain topologies into our standardized industrial data models.

Skills

Required

  • Python
  • SQL
  • REST APIs
  • ETL
  • Data Modeling
  • Knowledge Graph
  • Supply Chain Data Structures
  • ERP systems (SAP)
  • Logistics Visibility Platforms (FourKites, Project44)
  • Cloud Platforms (AWS, GCP, Azure)
  • DevOps (Git)

Nice to have

  • AWS
  • GCP
  • Azure
  • Oracle
  • MSSql
  • Big Data systems

What the JD emphasized

  • primary owner and architect
  • designing and packaging the "Data Pipelines"
  • powers industrial GenAI
  • build robust, standard integrations
  • structured , optimised and curated for consumption by the knowledge graph
  • optimised and packaged pipelines
  • own the evolution of our Integrated Supply Chain Solution Model
  • optimized for traversal by autonomous AI agents
  • product-ready
  • troubleshoot data pipeline issues
  • capture recurring issues, data quality gaps
  • translate these into improvements to the packaged integrations
  • build reliable, well-documented extractors
  • progressively enhance packaged connectors
  • Design, implement and package scalable data pipelines
  • ingest and normalize data
  • Enhance and refine the Integrated Supply Chain Solution Model
  • defining and packaging the relationships
  • Build and enhance the graph structures within CDF
  • allow AI agents to navigate
  • build high performant ETL
  • ensuring that industrial data types (time series and contextual events) are "agent-ready"
  • Develop transforms that adapt to data type and frequency
  • aligned with use case requirements
  • domain expert for customers
  • map their complex supply chain topologies
  • standardized industrial data models
  • support complex "Reasoning Workflows"
  • tool-calling capabilities
  • production-grade pipelines
  • External API Integration Expert
  • logistics visibility platforms
  • market intelligence syndicates
  • Supply Chain Domain Mastery
  • Deep understanding of supply chain data structures
  • Knowledge Graph & Relational Modeling
  • Expertise in designing complex data schemas
  • optimize models for both relational queries and graph traversals
  • Cloud-Native Tech Stack
  • Proficiency in Python, SQL
  • working knowledge of cloud platforms
  • DevOps Mindset

Other signals

  • industrial digitalization
  • AI agents
  • data pipelines
  • knowledge graph
  • supply chain