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.