What will your job look like? Design, build, and maintain robust and scalable data pipelines (batch and real-time) end-to-end. Design and implement scalable, flexible data architectures to support evolving business needs. Build and manage data platforms, including data lakes and data warehouses. Integrate multiple data sources (structured and unstructured) into a unified data platform using batch (ETL) and real-time streaming solutions. Design and implement efficient data models, schemas, and database structures (SQL / NoSQL). Develop and implement data quality processes to ensure accuracy, consistency, and reliability. Monitor, optimize, and troubleshoot data infrastructure to meet performance and SLA requirements. All you need is: 5+ years of hands-on experience as a Data Engineer, building data systems from scratch in dynamic environments. Bachelor’s degree in Computer Science, Engineering, or a related field (or equivalent practical experience). Strong proficiency in Python and advanced SQL, with solid experience in data modeling. Proven experience designing and building scalable data pipelines (batch and real-time), including streaming technologies such as Kafka. Strong experience working with AWS, including services such as S3, Athena and DynamoDB. Experience working with big data processing frameworks such as Spark, and columnar data formats (e.g., Parquet). Hands-on experience with workflow orchestration tools such as Airflow. Strong ownership and execution mindset, with excellent problem-solving skills and high attention to detail, and the ability to collaborate effectively and deliver in ambiguous, fast-paced environments. Experience with data platform technologies such as Databricks, Snowflake – Advantage. Experience building data platforms using modern lakehouse technologies (e.g., Iceberg) – Advantage. Fluent in English.
