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Strong brand, mid-level generalist title, metro location, and common 5–8 year band increase applicant competition.
Core data engineering and BI skills are broadly transferable across industries.
Explicit 5–8 years plus mandatory SQL, data-warehousing, big-data, BI tools, Git/CI/CD increases filter strictness.
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Lead design and implementation of data models, semantic layers, and full-stack data analytics solutions supporting business insights and strategy.
Manage end-to-end analytics projects including requirements gathering, planning, implementation, and automation to optimize data workflow efficiency.
Collaborate with cross-functional partners to ensure accuracy of business logic in data models and support large enterprise use cases like financial modeling and sales planning.
Bachelor's degree or higher in quantitative fields (Computer/Data Science, Engineering, Mathematics, Statistics).
5 to 8 years of relevant experience in business intelligence/data engineering.
Strong expertise in SQL, data warehousing (star schemas, slowly changing dimensions), ELT/ETL, MPP databases, and big data technologies (Spark, Hadoop, Snowflake).
Proficiency in BI tools (Looker, Tableau, PowerBI), analytics tools (Athena, Redshift, BigQuery, Snowflake), and general-purpose programming (Python, Scala, etc.).
Experienced in managing complex, large-scale analytics projects with strong ownership from design through execution in enterprise environments.
Skilled in building scalable automation frameworks that improve team productivity and enforce analytics development standards.
Experienced in cross-functional collaboration to translate business questions into precise data solutions, particularly in sales and financial analytics domains.