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Mid-level popular data role with general title, metro hiring, but Snowflake/dbt specialization reduces applicant pool.
Core Snowflake/dbt/Airflow data engineering skills transfer broadly, though banking regulatory knowledge raises domain specificity.
Explicit 5+ years plus mandatory Snowflake, dbt, Airflow, and Python skills enforce strict screening.
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Design, develop, test, deploy, and maintain scalable ELT data pipelines using dbt and Snowflake, ensuring efficient data transformation and flow.
Manage scheduling and orchestration of complex data workflows with Apache Airflow to guarantee timely data delivery and operational reliability.
Optimize Snowflake warehouse usage, enforce data governance, security protocols, and contribute to schema design and data loading strategies for cost-effective and scalable data platform management.
Bachelor/Master degree in Analytics, Data Science, Mathematics, Computer Science, Information Systems, Computer Engineering, or related technical field.
5+ years of hands-on experience with SQL and Data Warehouse/Data Lake technologies, including data loading, transformations, performance optimization, and security.
Proven expertise in building complex data transformation pipelines using dbt (including Jinja templating, macros, tests, documentation).
Strong experience with Apache Airflow for developing and deploying production-grade data pipelines and proficient Python scripting skills for data manipulation and API integrations.
Experienced in digital banking or financial services environment with understanding of financial data concepts and regulatory requirements.
Demonstrates deep technical mastery of ELT/ETL principles, data warehousing concepts, dimensional modeling, and data governance frameworks.
Capable of bridging technical and non-technical communication to solve business problems and uphold data best practices in a fast-paced, innovative engineering team.