Data Engineer

You’ll make a difference by:

• Designing, building, and maintaining scalable data products and data infrastructure on AWS
• Developing robust data pipelines using AWS-native services and integrating data across multiple application silos
• Supporting the automation, deployment, and operation of AI/ML workflows
• Defining data ingestion strategies, data formats and schemas, metadata/catalog integrations, federated data access, and end-to-end data product creation
• Enabling advanced analytics, AI/ML, and data-driven use cases by designing efficient data-access patterns and tooling (including support for LLM context engineering)
• Collaborating closely with Data Scientists, ML Engineers, and Product teams to translate business needs into scalable data solutions
• Actively participating in design discussions, technical reviews, and cross-team collaboration forums.

You’ll win us over by:

• Holding a Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline
• Having 3+ years of hands-on experience working with data at scale
• Strong experience in building and maintaining data pipelines
• Hands-on experience with AWS cloud-native services, such as:
  • Lambda, Glue, Athena, S3, SageMaker (or similar)
• Experience with Infrastructure as Code tools such as CDK, CloudFormation, or Terraform
• Proven experience in building and maintaining CI/CD pipelines
• Strong programming skills in Python and SQL
• Solid understanding of SQL and NoSQL databases, complex SQL queries, and performance optimization across large, distributed systems
• Practical experience with Apache Spark (preferably PySpark)
• Experience with REST APIs – usage, definition, and implementation
• Clear communication skills and the ability to capture and define technical requirements effectively.

You’ll stand out if you have:

• Experience with TypeScript and/or JavaScript
• Working knowledge of databases such as PostgreSQL, DynamoDB, or similar technologies
• Strong collaboration experience with Data Scientists and Machine Learning Engineers
• Interest or hands-on exposure to MLOps and the AI product lifecycle
• Domain experience (or strong willingness to learn) in IoT, time-series data, automation systems, digital twins, or smart buildings technologies.

We’ll support you with:

• Flexible and hybrid working opportunities
• A diverse, inclusive, and collaborative culture
• Continuous learning and development opportunities
• An attractive and competitive compensation package.