[Remote] Data Engineer
Note: The job is a remote job and is open to candidates in USA. Innodata Inc. is a global data engineering company that provides data, evaluation frameworks, and human expertise for building trusted artificial intelligence systems. The Data Engineer will design and build enterprise data warehouses, data lakes, and scalable ETL infrastructure on GCP and AWS, supporting data-driven decision-making and AI/ML use cases such as RAG, copilots, and agentic AI. The role also involves developing reliable pipelines, BI solutions, and governed datasets for supply chain and real estate operations.
Responsibilities
- 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
- Partner with supply chain, real estate, and AI/ML teams to provide pipelines for AI solutions (e.g., 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
Skills
- 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)
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