Senior Data Engineer – Cloud Data Platforms & Lakehouse

About AgentixLake

AgentixLake GmbH is a Data and AI engineering consultancy based in Hamburg, Germany. We design, build and operate production-grade data platforms and AI systems in our customers’ cloud accounts or on-premises environments. We are an AWS Services Partner and a Certified Services Partner in Anthropic’s Claude Partner Network. Our commercial focus is DACH and the Gulf region, alongside selected international opportunities.

Your role

Own the data foundations that analytics and AI depend on. You will lead the engineering of cloud data platforms and lakehouse solutions, from source integration and modelling through deployment, performance tuning and reliable operation.

Our data engineering services cover platform modernisation, analytics engineering and DataOps. We work with AWS, Snowflake, Databricks, Google Cloud and on-premises technologies, selecting the stack that fits the customer’s requirements.

What you will do

Design and deliver batch or streaming pipelines that integrate operational databases, APIs, files and event sources.

Build maintainable data models and transformations for analytics, governed metrics and AI applications.

Implement lakehouse or warehouse architectures, choosing appropriate storage formats, partitioning and processing strategies.

Establish data quality tests, orchestration, lineage, monitoring and recovery procedures; investigate incidents and remove recurring causes.

Manage infrastructure and deployments through version control, infrastructure as code and CI/CD, with appropriate access controls.

Lead technical delivery with customers and colleagues, review code and document the decisions needed to operate and extend the platform.

What you bring

Substantial experience delivering and operating production data platforms, including responsibility for technical decisions and service reliability.

Strong Python and SQL, sound data modelling and a practical understanding of distributed processing and orchestration.

Depth in at least one major environment such as AWS, Snowflake, Databricks or Google Cloud, with the ability to assess alternatives.

Experience with tools such as dbt, Airflow or Dagster, and with testing, Git, CI/CD and infrastructure automation.

The ability to explain performance and cost trade-offs, scope a deliverable and communicate clearly with technical and business stakeholders in English.

Experience with Apache Iceberg, Spark, Kafka, Terraform or data foundations for RAG and AI agents is valuable. AWS or other relevant certifications support your application; delivery evidence matters most.