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Mid-level analytics title, metro location, and broad dbt/SQL/Python requirements increase applicant competition.
dbt, SQL and Python analytics engineering skills are broadly transferable across industries.
Explicit 3+ years plus mandatory dbt, SQL, Python and cloud data warehouse experience applies strict filters.
Design, develop, and maintain scalable, documented data models using dbt to support business decision-making across multiple departments.
Develop complex SQL transformations and Python scripts for data processing, automation, quality validation, and analytics workflows on a cloud data platform.
Collaborate with Data Engineers and stakeholders to build reliable, cost-efficient data pipelines and maintain data quality and governance standards.
Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or related field.
3+ years of experience in Analytics Engineering, Data Analytics, Data Engineering, or related roles.
Strong hands-on experience with dbt, advanced SQL, Python programming, and cloud data warehouses (Snowflake, BigQuery, Redshift, or Databricks).
Experience with Git-based development workflows and CI/CD practices.
Experienced in building modular, tested, and version-controlled data models within modern cloud data platforms.
Skilled in dimensional modeling and implementing scalable analytics engineering solutions supporting cross-functional teams.
Able to optimize data pipelines focusing on performance, reliability, and cost efficiency while maintaining data quality and documentation standards.