Senior Data Engineer

Description

The company is an experience innovation company and a trusted partner to well-known brands, offering growth, international careers and a values-driven culture.

You would work in an environment built for ongoing learning and real impact, helping shape new digital solutions and customer experiences across industries.

The team values sharing, caring and daring, and its culture encourages creativity, diversity and autonomy.

A global, borderless setup makes collaboration across teams straightforward.

Responsibilities

• Design, build and tune cloud-based data platforms that support analytics, AI and data products.
• Look past each request to the business problem behind it, using analysis and experience to help shape requirements rather than only building them as written.
• Build batch, streaming and near-real-time pipelines that deliver governed, secure, observable, high-quality curated datasets.
• Create pipelines for machine learning, causal modeling and LLM applications such as RAG and agent-based systems, working with data scientists, ML engineers and platform teams.
• Work mainly on AWS with Databricks, with growing GCP work including cloud migration programs. Azure or Fabric experience is also valued. Go deep in one cloud, stay open to others, and expect projects that match your strongest platform, with room to grow.
• Build scalable platforms and pipelines, mainly on AWS and Databricks, and on GCP or Azure/Fabric where projects require it.
• Develop reliable batch, streaming and near-real-time pipelines with Spark and Delta Lake.
• Build ingestion, transformation and curation workflows for structured and unstructured data.
• Apply medallion layering (bronze, silver, gold) within reusable, scalable lakehouse designs that suit enterprise needs.
• Help move pipelines and data models from one cloud to another.
• Deliver model-ready, production-grade datasets for analytics, ML, causal modeling and optimization.
• Build pipelines for GenAI work, including LLM, RAG and vector-based data flows, plus agent-based architectures.
• Use Databricks MLflow and Unity Catalog, or equivalent tools on other platforms.
• Design logical and physical data models for analytical and operational needs.
• Orchestrate workflows with Databricks Workflows, Airflow, dbt or equivalents such as Azure Data Factory or Cloud Composer, focusing on automation, reliability and maintainability.
• Apply event-driven and streaming architecture patterns.
• Manage data governance, lineage, quality and access control (RBAC/ABAC) with Unity Catalog, AWS Lake Formation or equivalents such as Microsoft Purview or GCP Dataplex.
• Monitor data freshness, pipeline reliability and SLA adherence.
• Build serving layers such as APIs, feature inputs and analytical endpoints for downstream ML and AI platforms.
• Keep improving pipelines and infrastructure for performance, scalability and cost.

Requirements

• Ask why stakeholders need something, not only what they want.
• Point out gaps and better options instead of delivering a brief unchanged.
• Work with data scientists, ML engineers, analysts and business stakeholders.
• Help teams adopt data products and share good practice.
• Strong hands-on experience with Apache Spark and Delta Lake.
• Strong Python and SQL.
• A proven track record of building batch and streaming pipelines and production-grade data platforms.
• Solid understanding of data modeling, data quality and governance.
• Strong, demonstrable hands-on experience with the Databricks Lakehouse Platform, ideally on AWS (for example S3, Glue, EMR, Redshift, Kinesis, Lambda). This is a key requirement.
• Deep expertise in at least one major cloud (AWS, GCP or Azure/Fabric), plus demonstrable openness and ability to work across clouds.
• A strong Azure/Fabric background (Data Factory, Synapse, Fabric Lakehouse, Azure Databricks) is encouraged, provided you are keen to work in other clouds too.
• GCP experience (for example BigQuery, Dataflow, Pub/Sub, Cloud Storage, Dataproc) is highly valued, especially in migration work.
• Experience with lakehouse architectures and distributed systems.
• Strong understanding of scalability, reliability and performance.
• Naturally curious, strong at problem solving, and collaborative in cross-functional teams.
• Willing to challenge business requirements rather than accept them at face value.
• Adaptable and eager to work across platforms. Agile or consulting experience is beneficial.
• Hands-on experience across more than one cloud, or cloud migration experience (for example Azure to GCP).
• Snowflake experience.
• GenAI and AI data systems experience, such as RAG pipelines, vector databases and LLM data preparation.
• CI/CD for data pipelines and infrastructure as code (Terraform, CloudFormation).
• Streaming technologies (for example Kafka), Spark optimization, or ML and experimentation platforms.
• Experience building data products or large-scale analytics platforms.

Conditions

Hybrid work options are available.

The company is committed to inclusion and welcomes people from all backgrounds.

Applications are judged on skills, experience and potential, and no age, gender, marital status or headshot is needed.

Benefits

• A competitive compensation package.
• 25 vacation days.
• Co-subsidized transportation and Multisport cards.
• Premium health insurance with fast access to top healthcare services.
• Training support for technical and other skills events, courses and certifications.
• A career development plan shaped by performance reviews.
• A self-care program with psychological consultations and discussions for you and your team.
• Comfortable, well-designed office space.
• Team events and company gatherings.