Senior Data Engineer

Role overview

Design and improve cloud data platforms that support analytics, machine learning and data products. You’ll build dependable batch, streaming and near-real-time pipelines, while helping teams shape requirements and prepare governed datasets for AI applications such as retrieval-augmented generation and agent-based systems.

Responsibilities - Build scalable ingestion, transformation and curation pipelines for structured and unstructured data. - Implement lakehouse and layered data patterns using distributed processing and table formats such as Spark and Delta Lake. - Deliver production-ready datasets for analytics, machine learning, causal modeling and optimization. - Develop data workflows for generative AI applications, including retrieval, vector-based data flows and agent architectures. - Design analytical and operational data models and orchestrate workflows with suitable scheduling and transformation tools. - Apply governance, lineage, quality controls and access management; monitor freshness, reliability and service-level targets. - Optimize platforms for performance, scale and cost, and contribute to cloud migrations and data serving layers. - Work with business stakeholders, data scientists, machine-learning engineers and platform teams to clarify needs and recommend effective approaches.

Requirements - Senior-level experience engineering cloud-based data platforms and pipelines. - Deep experience with at least one major cloud platform; work may span AWS, Google Cloud or Azure. - Practical knowledge of distributed data processing and batch or streaming architectures. - Ability to design maintainable data models and automated workflows. - Understanding of data governance, security, quality and observability. - Strong analytical and communication skills, including the ability to question requirements and explain alternatives.

Nice to have - Databricks, Spark, Delta Lake, MLflow or Unity Catalog experience. - Familiarity with workflow tools such as Airflow or dbt, infrastructure as code, CI/CD, Kafka, vector databases or LLM data preparation. - Experience building data products, large-scale analytics platforms or cloud-to-cloud migrations.

Benefits and work setup - Hybrid work options, 25 vacation days, transportation and sports subsidies, and health coverage. - Training support, career development planning, wellbeing resources and team events.