Role Description 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. Qualifications - 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). Requirements - The expected salary range for this position is $100,000 – $120,000 USD per year, based on experience, skills, and qualifications. Company Description 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.

• Projetar e Desenvolver Pipelines de Dados: Arquitetar, implementar e manter pipelines ETL/ELT escaláveis e de alto desempenho para processar grandes volumes de dados estruturados e não estruturados. • Integração e Processamento de Dados: Construir arquiteturas acionadas por eventos para permitir o processamento de dados em tempo real e a integração perfeita entre sistemas. • Gerenciamento da Infraestrutura em Nuvem: Aproveitar os serviços em nuvem AWS (por exemplo, S3, Redshift, Glue, Lambda, Kinesis, Pentaho) para projetar e implantar uma infraestrutura de dados robusta. • Desenvolvimento e Otimização de Código: Escrever código limpo, eficiente e de fácil manutenção em Python para suportar a ingestão, transformação e orquestração de dados. • Colaboração em Ambientes Ágeis: Trabalhar em estreita colaboração com equipes multifuncionais, incluindo cientistas de dados, analistas e engenheiros de software, seguindo metodologias ágeis para entregar soluções iterativas. • Qualidade e Governança de Dados: Implementar melhores práticas para qualidade de dados, monitoramento e conformidade para garantir a integridade, consistência e segurança dos dados. • Otimização de Desempenho: Otimizar pipelines de dados e consultas em ambientes em nuvem para desempenho, escalabilidade e eficiência de custos. • Mentoria e Liderança: Fornecer orientação técnica aos membros juniores da equipe, promovendo uma cultura de aprendizado contínuo e melhoria. • Documentação e Compartilhamento de Conhecimento: Documentar designs técnicos, processos e fluxos de trabalho para garantir a manutenção e transferência de conhecimento.

• Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings. • Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value. • Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution. • Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake. • Partner closely with your peers under the Data Engineering Manager to share responsibilities across pipeline management, MLOps, and architecture, avoiding siloed knowledge and ensuring comprehensive team coverage. • Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts.

• Architect for the Future: Optimize our existing Snowflake architecture, establishing strict environmental isolation and scalable structures that prepare our data for eventual downstream commercialization and product offerings. • Drive Agentic Engineering: Leverage tools like Snowflake Cortex, Cursor, and UiPath to automate workflows, build semantic models, and deploy agents that accelerate time-to-value. • Establish Data Observability: Implement and manage robust data quality and observability frameworks to ensure pipeline reliability and proactive issue resolution. • Operationalize Machine Learning: Design and maintain MLOps pipelines to support the seamless rollout, monitoring, and lifecycle management of ML models directly within Snowflake. • Execute Shared Ownership: Partner closely with your peers under the Data Engineering Manager to share responsibilities across pipeline management, MLOps, and architecture, avoiding siloed knowledge and ensuring comprehensive team coverage. • Model for Enterprise Utility: Synthesize disparate operational entities into a unified, enterprise-wide semantic model that supports both internal analytics and future data monetization efforts.