Collaborate with product owners and team leads to design and implement new features that support evolving data needs.
Build and maintain scalable data pipelines and architecture for extracting, transforming, and loading data from various sources including APIs, streams, and data lakes.
Ensure data privacy and security compliance in all engineering solutions, aligned with internal policies and industry standards.
Monitor trends in data engineering and recommend tools or practices that align with organizational objectives.
Support the development and refinement of training resources by sharing technical knowledge and best practices with other teams.
Participate in tool selection and strategy planning for specific data integration and transformation scenarios.
Contribute to the development and enhancement of project monitoring through dashboards and reporting tools to support data-driven decision-making.
Qualifications
A Bachelor’s degree in Computer Science, Information Technology, or a related field is required.
Minimum of 3 years of commercial experience in data engineering.
Proficiency in Python programming for data processing and automation tasks.
Solid understanding of distributed computing concepts and technologies such as PySpark.
Experience with major cloud platforms (GCP, AWS, or Azure), particularly their data services.
Advanced knowledge of SQL and expertise in query optimization.
Strong grasp of data warehousing principles and data modeling techniques, including OLTP/OLAP systems, slowly changing dimensions (SCD), normalization/denormalization, and dimensional/star/snowflake schemas.
Proficiency in relational databases such as PostgreSQL, MSSQL, or MySQL.
Experience with data orchestration tools like Apache Airflow, Prefect, AWS Glue, or Azure Data Factory.
English language proficiency at a minimum B2 level.
Strong collaboration and communication skills; a reliable team player.
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