Innodata (Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked. Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted at scale.

We provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers. Scope of the Role: We are seeking a Data Engineer to design and build enterprise data warehouses, data lakes, and pipelines that power data-driven decision-making for data center supply chain and real estate operations.

This role is responsible for creating scalable, secure, and optimized ETL infrastructure on GCP/AWS, while enabling advanced AI/ML use cases such as RAG, copilots, and agentic AI for predictive analytics and workflow automation. What You’ll Own: Design and implement data-driven solutions on GCP including BigQuery, Cloud Storage, Dataflow, Pub/Sub, and Looker/BI. Build ETL scripts using SQL and Python to extract, clean, and transform structured and unstructured data from ERP, procurement, logistics, and facility management systems.

Develop and optimize data pipelines for ingestion, transformation, and loading into enterprise data lakes and warehouses. Build and extend end-to-end data and BI solutions, spanning extraction, storage, transformation, and visualization layers. , RAG ingestion, Copilot integration, multi-agent workflows).

Ensure data governance, lineage, and compliance across supply chain datasets. Continuously optimize query performance, ETL processes, and pipeline reliability. You’ll Thrive in This Role If You Have: Advanced proficiency in SQL (complex queries, optimization) and Python (data engineering, scripting, APIs).

Experience building ETL/ELT pipelines operating on structured and unstructured data sources . Knowledge of enterprise data warehouse and data lake architectures . Exposure to data pipelines for AI/ML (vector DB ingestion, embeddings, RAG pipelines, copilots, agents).

Familiarity with supply chain or data center operations data is a strong plus. Bonus: experience with ML Engineering, data visualization tools (Looker, Tableau, Power BI) and MLOps practices . Strong hands-on expertise with GCP services : BigQuery , Dataflow, Pub/Sub, Cloud Storage, Looker/BI (or similar, preferred).

Qualifications

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