The tasks awaiting you:

• Design, build, and operate scalable and reliable data pipelines using SQL and Python, ensuring efficient processing while optimizing performance and cost on platforms such as Snowflake and/or Databricks
• Develop and manage end-to-end data workflows by combining dbt-based transformations with schema migration tooling (e.g., SchemaChange or similar) and implementing efficient integration patterns between platforms (e.g., external tables, Snowpipe, connectors, or bulk loads)
• Create and maintain Snowflake stored procedures while orchestrating complex workflows using tools such as Airflow, ADF, or Databricks Workflows to ensure stable and automated data operations
• Operate and modernize existing legacy systems (e.g., PostgreSQL, Talend), supporting their gradual migration into cloud-based architectures across AWS and/or Azure, including core services like storage, compute, IAM, and networking
• Ensure high platform reliability by implementing strong data quality, observability, and monitoring practices (including alerting and SLAs), while applying security, governance, and CI/CD standards within Git-based development workflows

About you

• You have strong hands-on experience with Snowflake, including performance tuning, workload management, and cost optimization, as well as strong SQL and Python skills and proven experience building reliable, scalable data pipelines. Experience with Databricks is beneficial.
• You are experienced in dbt-based transformation workflows as well as schema migration approaches and understand how to integrate modern data platforms effectively
• You have a solid understanding of ETL/ELT concepts with a batch-oriented mindset and are open to evolving toward streaming, supported by strong data modeling fundamentals across normalized and analytical structures
• You are experienced in orchestration, cloud platforms (AWS and/or Azure), and the operation and modernization of legacy systems, and you follow best practices in CI/CD and Git-based development
• You bring a strong focus on data quality, observability, reliability, and platform governance, and would stand out with additional experience in Infrastructure-as-Code (e.g., Terraform), open table formats (Delta Lake, Iceberg), streaming technologies (e.g., Kafka, Event Hubs, Kinesis), FinOps practices, or data contracts and schema evolution