Data Engineer (Python + SQL / Sparql + Graph Data)

FI·Helsinkimid

Data EngineerData

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Quick Summary

Overview

Key Responsibilities Design, develop, and optimize ETL and data pipelines for large-scale data processing Write and optimize complex SQL queries including joins, subqueries, CTEs, window functions,

Technical Tools

Data EngineerData

Responsibilities

~1 min read

→Design, develop, and optimize ETL and data pipelines for large-scale data processing
→Write and optimize complex SQL queries including joins, subqueries, CTEs, window functions, and performance tuning
→Perform data transformation, validation, cleansing, and data quality checks
→Build and maintain scalable data ingestion pipelines using APIs, SFTP, Kafka, and related integration tools
→Work with large distributed datasets and ensure efficient data processing
→Design and implement graph data models including nodes, edges, and relationships
→Work with RDF data structures and semantic data frameworks
→Develop and optimize SPARQL queries for graph data retrieval and analysis
→Build and maintain knowledge graphs and ontology-driven data systems
→Automate data workflows and operational tasks using Python scripting
→Monitor, troubleshoot, and optimize pipeline performance and reliability

Requirements

~1 min read

Strong hands-on experience with Python, including Pandas, NumPy, and automation scripting
Advanced SQL expertise including query optimization, CTEs, subqueries, joins, and window functions
Strong understanding of ETL concepts, data engineering practices, and pipeline architecture
Experience working with large-scale distributed data processing environments
Solid understanding of graph data concepts including nodes, edges, and relationships
Experience with RDF data models and SPARQL querying
Experience with data ingestion and integration tools such as APIs, Kafka, and SFTP
Strong understanding of data transformation, cleansing, and performance optimization techniques

Nice to Have

~1 min read

Experience with graph databases such as Neo4j or Blazegraph
Exposure to cloud platforms including AWS, Azure, or GCP
Experience with workflow orchestration tools such as Airflow
Understanding of data lake and data warehousing concepts

Strong analytical and problem-solving skills
Ability to work in complex data-driven environments
Experience collaborating with cross-functional engineering and data teams
Self-driven mindset with strong attention to detail and performance optimization