Data Engineer/Graph Data Engineer

Data Engineer / Graph Data Engineer

Infosys is a global leader in next-generation digital services and consulting. We enable clients in more than 59 countries to navigate their digital transformation. With over four decades of experience managing complex enterprise environments, we help organizations modernize their technology landscapes and accelerate digital innovation. Our approach combines deep industry expertise with cloud, data, and AI capabilities to deliver resilient, future‑ready solutions.

Job Overview

Our client Infosys is seeking an experienced Data Engineer / Graph Data Engineer to join their data team in Helsinki. In this role, you will work on modern data solutions, building and optimizing scalable data pipelines, data models, and integration solutions.

Depending on your background and strengths, the role can be tailored with a focus on:

• Python & SQL-driven data engineering (ETL)
• Graph and semantic data (RDF, SPARQL)
• Or a combination of both

This is a fixed-term (approx. 6 months), full-time hybrid position based in Helsinki, Finland, starting as soon as possible.

About the role

In this role, you will work hands-on with data pipelines, transformation, and integration. You may also contribute to graph-based data modeling, knowledge graphs, and semantic data solutions, depending on your expertise.

Key responsibilities

Depending on your focus area, your role may include:

• Designing, developing, and optimizing ETL and data pipelines
• Writing and optimizing complex SQL queries (joins, CTEs, subqueries, window functions)
• Performing data transformation, validation, and data quality checks
• Working with large-scale datasets and distributed data environments
• Building and managing data ingestion pipelines (APIs, SFTP, Kafka)
• Automating workflows using Python scripting
• Monitoring and optimizing pipeline performance

Additionally (graph-focused roles):

• Designing and implementing graph data models (nodes, edges, relationships)
• Working with RDF and semantic data structures
• Writing and optimizing SPARQL queries
• Building and maintaining knowledge graphs and ontology-based systems
• Modeling relationships in connected datasets
• Supporting graph-based data visualization and user-oriented data solutions

What we expect

We are open to candidates with different strengths. You should have experience in at least one of the following areas:

Data engineering focus

• Strong Python skills (e.g. Pandas, NumPy, scripting)
• Advanced SQL skills
• Solid understanding of ETL processes and data pipelines
• Experience in data transformation, cleaning, and performance tuning
• Experience with data ingestion and integration tools

Graph / semantic data focus

• Understanding of graph data concepts (nodes, edges, relationships)
• Knowledge of RDF and SPARQL
• Experience with semantic data or linked data concepts
• Familiarity with graph databases (e.g. Neo4j, Blazegraph) is an advantage

General

• Experience working with large datasets
• Ability to work in modern data environments and integration landscapes

Nice to have

• Experience with cloud platforms (AWS, Azure, or GCP)
• Workflow orchestration tools (e.g. Airflow)
• Understanding of data lakes and data warehousing
• Experience with graph-based data visualization
• Basic UI/UX awareness, especially in data visualization contexts

What we offer

• A flexible role that can be shaped around your expertise
• Opportunity to work with modern data engineering and graph technologies
• A skilled and collaborative team
• Hybrid working model and competitive compensation

Interested? Please submit your application and CV along with your salary request in English as soon as possible. We will start the screening and interviewing process already during the application period. If you have questions related to this position, please contact Recruitment Consultant by email hannele.miettinen(at)staffpoint.fi 

Please note, that we do not accept applications via email or LinkedIn due to GDPR regulations. All applications should be submitted through our website.