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Mid-level generalist Data Engineer role in metro with broad skill requirements increases candidate competition.
Core data engineering skills are transferable but finance domain preference increases industry specificity.
Explicit 3–7 year requirement plus mandatory Python/SQL and specific data platform experience increases screening rigidity.
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Use Python and SQL to extract, clean, transform, and analyze business and platform data for actionable insights.
Build and maintain data pipelines, BI dashboards, and reports to support decision making and track key performance metrics.
Collaborate with cross-functional teams to gather requirements, address data quality issues, and improve analytical workflows.
3 - 7 years of hands-on experience in data analysis, reporting, or business intelligence.
Proficiency in Python for data processing and SQL with knowledge of data platforms like Snowflake, Databricks, or Redshift.
Experience building or contributing to data pipelines and BI dashboards using tools such as Power BI or Tableau.
Degree in Computer Science, Data Analytics, Statistics, or a related field.
Experienced in turning complex data into clear, actionable business insights focused on performance metrics.
Comfortable working hands-on with data engineering tasks including pipeline development and data quality management.
Background or experience in finance industry considered an advantage.