Data Engineer: We are seeking a Data Engineer with 5+ years of experience in data engineering, database development, and cloud-based data solutions, preferably on AWS. The ideal candidate will have strong SQL skills (T-SQL, PL/SQL) and hands-on experience with database technologies such as Oracle, SQL Server, Snowflake, Redshift, and Databricks. You will work with ETL/ELT tools and modern cloud integration services (e.g., AWS Glue, Apache Airflow, Azure Data Factory) and apply data modeling, data integration, and data warehousing concepts. Proficiency in Python for ETL pipeline development and knowledge of data governance, data quality, and GDPR security practices are required. Experience in Agile environments and collaboration tools is expected. Bonus: deep knowledge of data lake architectures, performance tuning for large datasets, metadata management, and real-time streaming frameworks (Kafka, Kinesis) and event pipelines. Experience with Snowplow or equivalent event tracking pipelines for behavioral analytics, and knowledge of big data processing frameworks (Spark/Flink) and CI/CD or Infrastructure-as-Code. You will design, develop, and maintain data pipelines (batch ETL and real-time streaming) to support product teams, implement and manage Snowplow-based event tracking, collaborate with cross-functional teams, generalize pipeline patterns for consistency, develop reusable data processing utilities in Python, and optimize database performance through indexing and query tuning.

Data Engineer: We are seeking a Data Engineer with 5+ years of experience in data engineering, database development, and cloud-based data solutions, preferably on AWS. The ideal candidate will have strong SQL skills (T-SQL, PL/SQL) and hands-on experience with database technologies such as Oracle, SQL Server, Snowflake, Redshift, and Databricks. You will work with ETL/ELT tools and modern cloud integration services (e.g., AWS Glue, Apache Airflow, Azure Data Factory) and apply data modeling, data integration, and data warehousing concepts. Proficiency in Python for ETL pipeline development and knowledge of data governance, data quality, and GDPR security practices are required. Experience in Agile environments and collaboration tools is expected. Bonus: deep knowledge of data lake architectures, performance tuning for large datasets, metadata management, and real-time streaming frameworks (Kafka, Kinesis) and event pipelines. Experience with Snowplow or equivalent event tracking pipelines for behavioral analytics, and knowledge of big data processing frameworks (Spark/Flink) and CI/CD or Infrastructure-as-Code. You will design, develop, and maintain data pipelines (batch ETL and real-time streaming) to support product teams, implement and manage Snowplow-based event tracking, collaborate with cross-functional teams, generalize pipeline patterns for consistency, develop reusable data processing utilities in Python, and optimize database performance through indexing and query tuning.