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In-demand mid-level data role with common Snowflake/SQL/Python skills yields moderate competition.
Core data engineering skills are highly transferable across industries despite preferred financial-domain experience.
Explicit 5+ years plus mandatory Snowflake, SQL, Python, ETL, and DevOps skills increases filter strictness.
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Design, build, validate, deploy, and maintain scalable ETL pipelines and governed data workflows using SQL, Python, MS SQL Server, and Snowflake.
Develop and manage dimensional data models and curated datasets to support analytics, reporting, and business intelligence.
Implement and maintain data quality, validation, and reconciliation processes ensuring trusted data outputs, collaborating with cross-functional teams in an Agile environment.
Bachelor’s degree in Computer Science, IT, or related field, or equivalent experience.
Minimum 5 years of professional experience as a Data Engineer or similar role with strong SQL and Python proficiency.
Hands-on experience with MS SQL Server and Snowflake, including implementing medallion architecture for data layers.
Experience with Azure DevOps or similar DevOps tools for CI/CD, source control, and deployment automation.
Experienced in designing and troubleshooting ETL pipelines and governed data workflows for enterprise-scale analytics environments.
Capable of implementing data governance and validation strategies to ensure accuracy and reliability of large datasets.
Comfortable working in Agile Scrum teams and collaborating effectively with data analysts, data scientists, and business stakeholders.