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Mid-level, popular Data Engineer title with broad Power BI/AWS requirements in metro increases candidate competition.
Core data engineering and Power BI skills are widely transferable across industries, so fit sensitivity is low.
Explicit 5–8 years plus mandatory Power BI, SQL, Python, and AWS skills make filtering fairly strict.
Design, develop, and maintain enterprise Power BI dashboards, reports, and semantic models with performance optimization and governance.
Build and manage scalable ETL/ELT data pipelines using AWS Glue, EMR, Lambda, and integrate multiple enterprise data systems including cloud platforms like AWS and Databricks.
Develop complex SQL queries, automate workflows with Python, maintain CI/CD and deployment pipelines, and ensure high availability and troubleshooting of analytics solutions.
5–8 years of experience in Power BI, Data Analytics, and Data Engineering.
Bachelor's degree in Computer Science, Information Technology, Engineering, or related field.
Proficient in Power BI (DAX, Power Query, RLS, incremental refresh), advanced SQL, Python programming, and AWS data services including Glue, Redshift, S3, Lambda.
Experience with cloud data warehousing, dimensional data modeling (Star/Snowflake schemas), and CI/CD pipeline implementation.
Deep expertise in Power BI enterprise deployments including governance and performance tuning within a cloud ecosystem, preferably AWS.
Strong skills in building scalable ETL pipelines, data lake and Lakehouse architectures, integrating diverse data sources, and managing workflows in a production environment.
Experience operating in Agile teams with knowledge of version control, automated deployment, and cross-functional collaboration with business stakeholders.