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High—Tier-1 brand, mid-level generalist role, metro appeal, and broad skill set increase applicant competition.
Medium—core data engineering skills are transferable, but consulting/client-management and high travel requirements increase specificity.
Medium—explicit minimum experience and broad technical stack expectations, but no rigid certifications mandated.
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Lead data engineering projects focused on strategic insights and business performance optimization, utilizing ETL/ELT integration and advanced analytics.
Manage and mentor data engineering teams, oversee project planning, budgeting, execution, and completion ensuring quality delivery and client satisfaction.
Develop and implement innovative data architectures and pipelines using tools and platforms like DataStage, AWS (S3, Glue, Redshift), SQL Server, DB2, and support scalable data visualization solutions.
Bachelor's degree required.
Minimum 4 years of professional experience in data engineering or related fields.
Experience with ETL/ELT processes, data pipeline architecture, and cloud analytics platforms (specifically AWS & Redshift).
Travel requirement up to 60%.
Experienced in managing and mentoring teams within data engineering or analytics projects, demonstrating leadership in delivery and performance management.
Proficient in business intelligence tools and languages such as BIRT, Python, Java, QlikView, Spotfire, and data architecture design.
Has a background or education in Computer Science, Economics & Finance, Operations Management, Statistics, or Engineering, with a strong focus on continuous process improvement in data analytics.