Posted 6 days ago
Data Lakehouse Developer
AI Summary
Designs and maintains a scalable Data Lakehouse architecture using Apache Iceberg, Delta Lake, or Apache Hudi, builds PySpark pipelines for MongoDB integration, optimizes Spark performance and costs, and implements data quality monitoring and CI/CD automation on Kubernetes.
About this role
We are part of an international organization listed on Nasdaq Stockholm, operating in 11 countries. In Poland, we work with full autonomy. We independently choose our technologies, processes, and ways of working while maintaining the highest professional standards.
We deliver a wide range of IT projects in Poland and internationally, across multiple domains and technologies. This allows our consultants to develop their competencies and gain experience aligned with their professional ambitions.
Our collaboration is built around the CARE philosophy. For us, it means responsible project matching, transparent cooperation, and a long-term approach to professional growth.
Join us as Data Lakehouse Developer. The project focuses on building and maintaining a scalable Data Lakehouse architecture—using Apache Iceberg, Delta Lake, or Apache Hudi—to process data across the entire pipeline, from raw lake ingestion to refined presentation layers
Grow in an environment that cares about your development as much as you do.
Your responsibilities
Data Lakehouse & Architecture: Design and maintain versioned data layers (Bronze, Silver, Gold) using Apache Iceberg, Delta Lake, or Apache Hudi, including CI/CD for schema evolution.
Spark & MongoDB Integration: Build PySpark/Spark-SQL pipelines for data reconciliation between MongoDB and source systems, optimizing reads using partitioning and match push-down.
Performance & Cost Tuning: Optimize Spark jobs (AQE, caching, broadcast joins) to lower latency and minimize Data Lakehouse processing costs.
Quality, Monitoring & SLAs: Implement automated data quality checks, SLA/SLO tracking, and distributed monitoring for batch/streaming pipelines using Prometheus, Grafana, and Dynatrace.
Kubernetes & Automation: Automate Spark application deployments on Kubernetes using Docker, Spark Operator, and CI/CD pipelines.
Delivery & Collaboration: Diagnose data discrepancies, maintain technical runbooks, conduct code reviews, and collaborate in Agile teams (Scrum/Kanban).
Requirements
4+ years of hands-on production experience in data engineering.
Strong proficiency in Python, PySpark, Spark-SQL, DataFrames, and Structured Streaming.
Proven experience with MongoDB (Spark Connector, indexing) and Apache Iceberg (table versioning, upserts/merges).
Hands-on background in CI/CD automation (Jenkins) and containerized deployments on Kubernetes.
You need to live in Poland and/or be eligible to work here.
What we offer
• Work in a Scandinavian culture built on logic and dialogue.
• Health and sports benefits tailored to your needs.
• Speed Coaching – short sessions that provide new perspectives and solutions.
• Sports training, an internal team, and opportunities to take part in competitions.
• Team events and development-focused meetups.
Apply today!
Skills
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