Data Platform Engineer

• Data Platform Development and Maintenance: Design, construct, and sustain reliable, high-performance data platforms. Optimize end-to-end data pipelines and automate workflows for maximum efficiency.
• Cross-Functional Collaboration: Partner with Product, Engineering, and Data Science teams to evolve and implement a cohesive data platform strategy, translating conceptual needs into tangible, practical solutions.
• Data Governance and Documentation: Establish and enforce company-wide data standards. Maintain comprehensive documentation and manage the central data registry to ensure data integrity and accessibility.
• User Support and Communication: Provide training and support to platform users. Disseminate platform updates, key metrics, and insights via effective channels, including dashboards and automated notification systems (e.g., bots).
• Platform Implementation and Best Practices: Plan and execute major, organization-wide platform transitions. Ensure the consistent application of best practices across the entire engineering lifecycle, including coding, testing, deployment, and long-term maintenance.
• Insight-Driven Engineering: Utilize data as the fundamental driver for all engineering efforts, guaranteeing that outcomes are informed by actionable insights.

Salary: monthly gross SEK 42000 (40 hours a week)

Requirements:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.
• 5+ years of experience in Data Engineering, Big Data, Backend Engineering, or Data Platform development.
• Strong programming skills in Python; experience with Go, Java, or JavaScript/TypeScript is considered an advantage.
• Hands-on experience with large-scale data platforms and distributed data processing technologies such as Kafka, Spark, Airflow, and ETL/ELT pipelines.
• Experience working with relational and non-relational databases, including PostgreSQL, MySQL, MongoDB, Cassandra, Elasticsearch, or similar technologies.
• Experience with cloud environments.
• Familiarity with DevOps and infrastructure tools.
• Experience designing and optimizing scalable data pipelines and backend systems.
• Understanding of data governance, data quality, and platform best practices.
• Experience collaborating with cross-functional teams including Product, Engineering, and Data Science.
• Strong analytical, problem-solving, and communication skills.
• Experience with AI/ML infrastructure or data-driven systems is considered an advantage.
• Professional proficiency in English.