Data Engineer

Location: Helsinki, Finland

Department: Data Engineering

Location Type: HYBRID

Employment Type: FULL_TIME

• Run discovery with clients to understand their data landscape, needs and constraints.
• Propose suitable data stacks, architectures and patterns (e.g. Lakehouse, Data Mesh, modern ELT pipelines).
• Help clients move towards a “data as a product” mindset: ownership, lifecycle and value.

• Design and implement robust, scalable data pipelines and platforms in the cloud.
• Work with modern data stacks such as Snowflake, Databricks, BigQuery and similar.
• Use orchestration and transformation tools (e.g. Airflow, Azure Data Factory, dbt, cloud-native workflows) to automate and harden data flows.
• Ensure data is reliable, well-structured and accessible for analytics and AI use cases.

• Build, test, deploy and maintain production-grade pipelines using modern engineering practices (version control, CI/CD, infrastructure as code).
• Optimise performance and cost, especially in SQL, storage and compute usage.
• Balance batch and streaming approaches, and advise clients when real-time is (and is not) needed.

• Work together with data scientists, ML engineers and AI architects so that models have the data foundations they need.
• Translate business requirements into technical designs and implementation plans.
• Guide and coach client data teams during implementation and handover.

• Help clients set up sensible practices around data quality, lineage and access control.
• Contribute to decisions on cataloguing, governance tooling and security patterns.
• Make sure solutions are compliant and maintainable.

• Join early client discussions to shape cases from a data engineering and architecture perspective.
• Explain modern data approaches in clear, grounded terms to both technical and non-technical stakeholders.
• Help identify new opportunities where better data foundations enable meaningful AI and analytics.

• • One or more of: Snowflake, Databricks, BigQuery, Redshift or similar.
  • Strong SQL skills and experience with cloud data warehouses, data lakes or Lakehouse architectures.

• • Building and maintaining ETL/ELT pipelines at scale.
  • Experience with orchestration tools such as Airflow, Dagster, Azure Data Factory, Dataflow, or cloud-native schedulers.
  • Familiarity with transformation frameworks such as dbt is a clear plus.

• • Strong skills in Python and SQL; version control with Git.
  • Experience with PySpark or Scala is useful.
  • Understanding of testing (e.g. pytest), code quality and optimisation, especially for SQL and data-heavy workloads.

• • Experience with at least one major cloud (AWS, Azure or GCP).
  • Familiarity with cloud-native data services (e.g. Glue, Synapse, Dataflow, S3/ADLS, Pub/Sub, etc.).
  • Infrastructure as code (Terraform, CloudFormation or similar) is a plus.
  • Experience with Docker and Kubernetes is beneficial but not mandatory.

• • BI tools (Power BI, Looker, Tableau or similar).
  • Data governance, cataloguing and lineage (e.g. Unity Catalog, Collibra or similar).
  • Streaming technologies (Kafka, Kinesis, Pub/Sub) and knowing when streaming adds real value versus batch.

• Comfortable working directly with CxO and business stakeholders, not only IT.
• Able to lead or co-lead workshops and requirements discussions.
• Capable of translating ambiguous business problems into concrete data architectures and implementation plans.
• Willing to be hands-on in implementation; this is not a pure architecture or “slideware” role.
• Calm in ambiguous environments with shifting requirements; takes initiative rather than waiting for perfect specifications.
• Collaborative, with an entrepreneurial mindset and a realistic understanding of small-company life (and its benefits).

• You get to shape the data strategy and architecture for multiple clients, not just maintain a single internal platform.
• You influence tool choices, patterns and stack decisions, as long as they support real business value.
• You design and build the data foundations that enable meaningful AI and analytics, not “AI for the sake of AI”.
• You help clients rethink how they treat data – ownership, operations and architecture – towards a product-centric approach.
• You see the full cycle: from discovery and design to a working pipeline MVP and beyond.

• Location: You must be based in Finland and have a valid work permit in Finland.
• Office presence: Ability to visit our Helsinki office roughly once a week (sometimes more depending on client and project needs).