The Role
Consulting Senior Data Engineer: lead client data discovery, design cloud data platforms and production-grade ETL/ELT pipelines, implement orchestration and IaC, optimize performance/cost, collaborate with AI/data teams, advise on governance, and support sales and client workshops.
Summary Generated by Built In
About the role
We are looking for a Senior Data Engineer to design and build modern data solutions for our clients and to act as a key technical voice in AI-driven transformations.
You will work directly with business and technical stakeholders: discovering current data reality, proposing suitable architectures and stacks, and delivering production-grade data pipelines. You will also help clients think about data as a product: what their core data assets are, who owns them, and how those should be developed over time to support AI and analytics.
This is a consulting role. You will both advise on data strategy and architecture, and implement the solutions yourself.
What you will do
Lead client data discovery and solution design
- 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 build modern data platforms
- 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.
Own the end-to-end pipeline lifecycle
- 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.
Collaborate closely with AI, data and business teams
- 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.
Advise on data governance, quality and security
- 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.
Support sales and act as an advocate for modern data engineering
- 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.
What you bring
Technical skills
You do not need to tick every box, but you should be confident in several of these areas and comfortable learning the rest.
Solid experience with modern cloud data platforms
- One or more of: Snowflake, Databricks, BigQuery, Redshift or similar.
- Strong SQL skills and experience with cloud data warehouses, data lakes or Lakehouse architectures.
Data pipelines and orchestration
- 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.
Programming and software engineering practices
- 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.
Cloud platforms and DevOps mindset
- 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.
Additional relevant areas (nice to have, not required)
- 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.
Consulting skills and mindset
- 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).
Why this role might be interesting for you
- 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.
Practicalities
- 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).
How to apply
If this sounds like you, send us your CV and we’re happy to tell you more! We review applications continuously.
Skills Required
- Senior-level data engineering experience
- Experience with one or more cloud data platforms (Snowflake, Databricks, BigQuery, Redshift)
- Strong SQL skills
- Building and maintaining ETL/ELT pipelines at scale
- Experience with orchestration tools (Airflow, Dagster, Azure Data Factory, Dataflow, cloud-native schedulers)
- Familiarity with dbt
- Strong Python skills
- Experience with PySpark or Scala
- Version control with Git
- Understanding of testing (e.g., pytest), code quality and optimisation for data workloads
- Experience with at least one major cloud (AWS, Azure or GCP)
- Familiarity with cloud-native data services (Glue, Synapse, Dataflow, S3/ADLS, Pub/Sub)
- Infrastructure as code experience (Terraform, CloudFormation or similar)
- Experience with Docker and Kubernetes
- Experience with BI tools (Power BI, Looker, Tableau) or similar
- Experience with data governance, cataloguing and lineage tools (Unity Catalog, Collibra or similar)
- Experience with streaming technologies (Kafka, Kinesis, Pub/Sub)
- Consulting skills: comfortable working with CxO, leading workshops and translating business needs
- Based in Finland with a valid work permit in Finland
- Ability to visit the Helsinki office roughly once a week (on-site presence)
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The Company
What We Do
Renessai is a strategic AI consultancy based in Helsinki, Finland. Founded in 2024, the company helps organizations navigate AI hype by combining strategy, change leadership, and technical expertise to create measurable business value. They work with leadership teams to audit AI capabilities, prioritize use cases, and ensure concrete outcomes, rather than selling software or technology products.
