Data Engineer

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. 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. 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). Please be aware of recruitment scams involving individuals or organizations falsely claiming to represent employers. Innodata will never ask for payment, banking details, or sensitive personal information during the application process. To learn more on how to recognize job scams, please visit the Federal Trade Commission’s guide at https://consumer.ftc.gov/articles/job-scams. If you believe you’ve been targeted by a recruitment scam, please report it to Innodata at [email protected] and consider reporting it to the FTC at ReportFraud.ftc.gov.

Design and implement scalable data pipelines, warehouses, and lakes on GCP/AWS to support supply chain and real estate operations. Build and optimize ETL processes and BI solutions to enable advanced AI/ML use cases like RAG and agentic workflows.

Requires advanced proficiency in SQL and Python along with experience building ETL/ELT pipelines for structured and unstructured data. Candidates should have knowledge of data warehouse architectures and exposure to AI/ML data pipelines.

Identified from the job description. Confirm important requirements above.

• SQL
• Python
• ETL/ELT Pipelines
• GCP
• BigQuery
• Dataflow
• Pub/Sub
• Cloud Storage
• Looker
• Data Warehousing
• Data Lakes
• RAG Pipelines
• Vector DB Ingestion
• Machine Learning Engineering
• MLOps
• Data Visualization

• Data & Analytics
• Technology
• Software
• Engineering
• Logistics

Minimum experience

0+ years

Posting language

English

Working hours

40 hours per week

Seniority

Entry level

Application method

Direct apply is available