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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Tiger Analytics - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Automated Storage and Retrieval Systems, Data Architecture, Data Cleansing, Data Infrastructure, Data Integration, Extract Transform Load (ETL), Data Systems, Machine Learning, SQL Databases, Unstructured Data, Data Processing, System Availability, Large Language Models, Apache Spark, Generative AI, AWS Lambda, Git, Data Lakes, Infrastructure Automation Frameworks, AWS Glue, Machine Learning Operations, Software Version Control, Data Pipelines, Amazon Redshift, Databricks - **Published:** September 27, 2026 - **Apply:** https://www.juju.com/job/16_e1ee88ad6 ## About the Role * 8+ years experience as a Data Engineer working with AWS cloud services. * Hands-on experience with AWS services such as S3, Glue, Lambda, Redshift, and related data platform tools. * Experience building data pipelines using Databricks, Apache Spark, and SQL. * Experience with Apache Airflow for workflow orchestration. * Strong understanding of data modeling, data lake/lakehouse architectures, and ETL/ELT frameworks. * Experience with CI/CD pipelines and version control systems (Git). * Exposure to Generative AI or LLM-based applications. * Experience supporting data pipelines for AI/ML workloads. * Familiarity with vector databases, embeddings, and Retrieval-Augmented Generation (RAG) architectures. * Experience working with LLM APIs or AI frameworks such as LangChain. * Understanding of MLOps workflows and model deployment pipelines. ## Description We are seeking an experienced Data Engineer to join our data team. In this role, you will be responsible for designing, building, and maintaining scalable data pipelines, data integration processes, and data infrastructure on AWS cloud. You will collaborate closely with data scientists, analysts, and AI teams to support analytics, machine learning, and Generative AI initiatives across the organization., * Design, develop, and deploy end-to-end data pipelines on AWS cloud infrastructure using services such as Amazon S3, AWS Glue, AWS Lambda, Amazon Redshift, etc. * Implement data processing and transformation workflows using Databricks, Apache Spark, and SQL to support analytics, reporting, and AI-driven use cases. * Build and maintain orchestration workflows using Apache Airflow to automate data pipeline execution, scheduling, and monitoring. * Support data preparation and ingestion for AI/ML and Generative AI workloads, including handling structured and unstructured datasets. * Enable data pipelines that support LLM-based applications, vector embeddings, and knowledge retrieval systems. * Lead the migration of legacy data systems to modern cloud-based data architectures. * Develop and maintain CI/CD pipelines for data workflows and platform automation. * Collaborate with data scientists, ML engineers, and AI teams to ensure data availability for model training, inference, and GenAI applications. * Optimize data pipelines for performance, reliability, scalability, and cost-effectiveness using AWS best practices. ## Related Videos - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [Fully Orchestrating Databricks from Airflow](https://www.wearedevelopers.com/videos/336-fully-orchestrating-databricks-from-airflow) - [How we built an AI-powered code reviewer in 80 hours](https://www.wearedevelopers.com/videos/1511-how-we-built-an-ai-powered-code-reviewer-in-80-hours) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Tips, Techniques, and Common Pitfalls Debugging Kafka](https://www.wearedevelopers.com/videos/838-tips-techniques-and-common-pitfalls-debugging-kafka) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Data Engineer Salary UK](https://www.wearedevelopers.com/magazine/253-data-engineer-salary-uk) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production)