Senior Data Developer

This is a fully remote position, open to applicants in Poland.

📋 Description

• Design, develop, and maintain ETL/ELT processes utilizing SQL, PySpark, and Python, incorporating data from various source systems.

• Create data layers in Databricks, Data Lake, and Delta Lake, which includes tables, views, and data models tailored for analytical, reporting, and automation purposes.

• Automate and orchestrate the loading and transformation processes of data.

• Guarantee data quality, consistency, and reliability through validation rules, monitoring, alerting, and incident diagnosis.

• Enhance SQL queries, Spark processes, and data storage structures for improved performance, stability, scalability, and cost-effectiveness.

• Supply dependable, ready-to-use data to analysts, Product Owners, and other stakeholders.

• Develop and maintain technical documentation in Confluence that details data processes, models, KPI logic, dependencies, data lineage, and incident-handling protocols.

• Leverage AI tools as a productivity booster while ensuring the accuracy of generated code, configurations, and documentation prior to implementation.

⛳️ Requirements

• Minimum of 2 years of experience in Data Engineering, Analytics Engineering, or data analysis.

• Proven experience in designing ETL/ELT processes and constructing data models or layers for analytical objectives.

• Experience in maintaining production data processes, with a focus on monitoring, issue diagnosis, and ensuring data quality.

• Familiarity with cloud solutions, particularly Microsoft Azure.

• Strong practical knowledge of SQL, Python, PySpark, and Databricks.

• Understanding of Data Lake / Delta Lake architecture and principles of data modeling.

• Hands-on experience with Git and Azure DevOps, including managing changes across Development, Testing, and Production environments.

• Capability to translate business requirements into technical solutions.

• Proficient in English for effective communication in an international setting.

• Possess analytical and logical thinking skills, attention to detail, proactivity, and the ability to prioritize tasks under pressure.

• Nice to have: experience in a complex operational environment.

• Nice to have: knowledge of dimensional modeling, including star schema, fact and dimension tables, data grain, and normalization or denormalization techniques for reporting and analytics.

• Nice to have: advanced knowledge of Databricks and Delta Lake functionalities.

• Nice to have: experience with Kafka, Structured Streaming, or Event Hubs.

• Nice to have: familiarity with monitoring and alerting tools.

• Nice to have: understanding of data security, access control, metadata management, and data lineage concepts.

• Nice to have: experience with Jira and Confluence.

• Nice to have: Microsoft Certified: Fabric Analytics Engineer DP-600 or Databricks Data Engineer / Analyst Associate certifications.

🏝️ Benefits

• Genuine ownership — your data products will have a direct impact on strategic decisions.

• Opportunity to collaborate in a diverse, international, and cross-functional environment with leading experts.

• Freedom to experiment with new technologies, including AI tools, and implement innovations in production.

• Immediate visible impact.

• B2B type of collaboration.