Senior Analytics / Data Engineer — Cloud, Databricks & Data Products

We are building a data engineering and analytics engineering team responsible for data used by product, operational and decision-making teams. This is not about adding more tables to a warehouse. It is about building a data platform people can trust.

You will work on systems that collect data from multiple sources, structure it, model it, expose it for analytics and power digital products. SQL, cloud, Databricks, data quality, performance, cost and architecture all matter here.

This role is for someone who likes seeing the whole data flow. From ingestion, through transformations and models, to dashboards, APIs and decisions made on top of data. If you only want to move pipelines from point A to point B, this may not be the best fit. If you want to build data products that make sense technically and commercially, keep reading.

• Data pipelines for a large enterprise platform.
• Ingestion solutions for batch and near real-time data.
• Data models that structure complex business processes and metrics.
• A single source of truth layer for key KPIs, definitions and dimensions.
• Solutions based on Databricks, Spark, SQL, Python and cloud data platforms.
• Data transformations in dbt, Dataform or similar tools.
• Performance layers for dashboards, reports and data products.
• Integrations with internal systems, APIs and external data sources.
• Monitoring mechanisms for data quality, cost, latency and pipeline reliability.

• Design and develop scalable data pipelines.
• Build data ingestion from multiple sources, both batch and near real-time.
• Model data in a way that gives clear metric definitions and reduces reporting chaos.
• Create transformations in SQL, Python, Spark/PySpark, dbt, Dataform or similar tools.
• Work with Databricks and lakehouse architecture.
• Optimize cost, processing time, latency and pipeline stability.
• Design data layers for dashboards, reports, analytical products and APIs.
• Implement data quality tests, monitoring, alerts and validation mechanisms.
• Make architectural decisions around data models, tools and processing patterns.
• Collaborate with Product, Analytics, Backend, Cloud Engineering and business stakeholders.
• Translate ambiguous business needs into concrete models, pipelines and data contracts.
• Maintain technical documentation, metric definitions and data engineering standards.

• Data engineering

  Databricks, Apache Spark, PySpark, SQL, Python

• Analytics engineering

  dbt, Dataform, data modelling, metrics layer, semantic layer

• Cloud

  Azure, AWS or GCP, cloud data platforms, serverless components

• Storage / processing

  Delta Lake, data lakehouse, data warehouses, object storage

• Orchestration

  Airflow, Databricks Workflows, cloud-native schedulers or similar

• Infrastructure

  Terraform, CDK, Infrastructure as Code

• Data quality

  tests, monitoring, lineage, observability, validation checks

• BI / consumption

  dashboards, analytical APIs, data marts, reporting layers

• Collaboration

  Git, CI/CD, code review, documentation, stakeholder workshops

• At least 5 years of experience in data engineering, analytics engineering, BI engineering or a similar role.
• Strong SQL skills and practical experience with data modelling.
• Experience with Databricks, Apache Spark or PySpark.
• Good Python skills in a data processing context.
• Experience with cloud data platforms, preferably Azure, AWS or GCP.
• Practice building ETL/ELT pipelines for production systems.
• Knowledge of dbt, Dataform or similar transformation tools.
• Ability to design data models that are understandable, performant and maintainable.
• Experience with Infrastructure as Code, such as Terraform or CDK.
• Understanding of data quality, testing, monitoring and data incident handling.
• Comfortable communication in English with technical and non-technical stakeholders.
• Independence in making decisions and prioritizing work.

• Experience with Delta Lake, lakehouse architecture or medallion architecture.
• Work with Databricks Workflows, Airflow or other orchestration tools.
• Knowledge of semantic layer, metrics layer or data contracts.
• Experience with CDC, streaming or near real-time integrations.
• Practice with dashboards, BI and reporting layers.
• Experience building data products from scratch.
• MLOps or collaboration with ML/AI teams.
• Knowledge of multiple clouds or data platform migrations.
• Experience in enterprise or regulated environments.

• Remote-first setup and flexible working hours.
• Work on a data platform that supports real processes and real users.
• Participation in decisions about data architecture, tooling and standards.
• Opportunity to build solutions end-to-end, from ingestion to data consumption layers.
• Budget for equipment, training, conferences and cloud/data certifications.
• Access to cloud environments, Databricks and developer tools.
• Room for proper data design, not just firefighting broken pipelines.
• Private medical care and benefits package.
• Short recruitment process based on real data engineering problems.

We are looking for someone who understands that a good data platform is not just a working pipeline.

It is metric definitions, data contracts, quality, lineage, cost, performance, monitoring, documentation and user trust in data.

If you enjoy building data systems that do not just process information, but actually help people make decisions, we should talk.