Data Engineer (m/f/d)
Indexed description
You will work with a modern tech stack (Dagster, dbt, Clickhouse, AWS) to build the pipelines that power our analytics, machine learning, and GenAI products. If you care about code quality, automation, and "Data as a Product," this role is for you.
Our Tech Stack
- Languages: SQL, Python
- Orchestration: Dagster (migrating from Airflow).
- Data Stores: Redshift, Clickhouse, S3.
- Transformation: dbt, Fivetran.
- Cloud & Infra: AWS (ECS/EKS, Glue, Lambda, Athena)
- IaC: Terraform with Terragrunt.
- AI/GenAI: AWS Bedrock, LangChain, LLMs.
- Integrate GenAI capabilities (LLMs, LangChain) into our engineering workflows.
- Develop and maintain reliable ETL/ELT pipelines using SQL and Python.
- Use dbt to model raw data into clean, business-ready datasets (Star Schema) that enable stakeholders to self-serve.
- Own the quality of your data. Implement tests (dbt tests, unit tests)
- Work with AWS services (S3, DMS, Glue) and containerized environments (Docker/Kubernetes) to deploy your code.
- Partner with Data Scientists and Product Managers to understand their data needs and deliver high-quality solutions.
- 3+ years of hands-on experience in Data Engineering.
- Familiarity with LLMs, AWS Bedrock, or LangChain
- Experience or exposure to Retrieval-Augmented Generation (RAG).
- Experience automating workflows using AI and integrating LLM capabilities into engineering or data pipelines.
- You treat data pipelines like software products. You are comfortable with Version Control (Git), code reviews, and testing.
- You can write complex, efficient queries and understand data modeling concepts
- You can write clean Python scripts for data manipulation and automation (beyond just "notebook scripting").
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