Senior Data Engineer (Spark, Iceberg, AWS)

Senior Data Engineer – Apache Spark / Iceberg / AWS S3

Location: Remote, Finland

Contract: 12 months, extendable

Working Model: Remote

We are looking for an experienced Data Engineer to join a data engineering team responsible for building scalable data pipelines and curated datasets for analytics and machine learning use cases.

The role requires strong hands-on experience with Apache Spark, SQL, AWS S3 and data lakes, with Apache Iceberg and Dremio experience being highly desirable.

Key Responsibilities

• Develop scalable data pipelines using Apache Spark to transform raw data into curated datasets.
• Analyse raw data structures through Dremio and identify appropriate source tables, fields, joins, filters and business keys.
• Build consistent offline training datasets and batch-scoring datasets using governed feature definitions.
• Work with Data Scientists to ensure reliable and reusable data for machine learning use cases.
• Analyse existing data flows and identify improvements across data quality, performance, lineage and business logic.
• Collaborate with Data Scientists, Data Architects, Business Analysts, Product Owners and source-system experts.
• Validate data definitions, transformation logic and business requirements.
• Work with data lakes and data marts, ensuring data is structured and optimised for downstream consumption.
• Contribute to data-platform, feature-store, MLOps and lakehouse initiatives.
• Continuously improve data engineering practices, automation and pipeline performance.

Essential Skills & Experience

• 6–12 years of Data Engineering experience.
• Strong hands-on experience with Apache Spark.
• Strong SQL skills, preferably including Oracle SQL.
• Experience working with AWS S3 and cloud-based data platforms.
• Strong understanding of Data Lakes and Data Marts.
• Experience with Apache Iceberg.
• Good understanding of Unix/Linux environments and command-line operations.
• Experience with AWS DevOps and automation.
• Strong understanding of data transformation, ETL/ELT and scalable data pipelines.
• Ability to work with complex data structures and identify appropriate joins, filters and business keys.
• Strong communication and collaboration skills.

Desirable Skills

• Hands-on experience with Dremio.
• Experience with Feature Stores and ML data pipelines.
• Knowledge of MLOps.
• Experience with Lakehouse architectures.
• Exposure to batch scoring and machine learning feature engineering.
• Experience with data lineage and data-quality frameworks.