Data Analyst

Principal Financial Group
1d$70,000 - $94,000Hybrid

About The Position

Operating at the intersection of financial services and technology, Principal builds financial tools that help our customers live better lives. We take pride in being a purpose-led firm, motivated by our mission to make financial security accessible to all. Our mission, integrity, and customer focus have made us a trusted leader for more than 140 years. As Principal continues to modernize, we are seeking a Data Analyst II to join our Data & Analytics Participant Marketing Team. In this role, you will drive the development of data solutions that influence business decisions and address key challenges. You’ll demonstrate your business and technical competence to uplift a new line of business for our department, deliver data-driven solutions, and collaborate closely with partners to ensure alignment with strategic objectives.

Requirements

  • Bachelor Degree in a Business or Technical field plus 2-3 years of related analytics, consulting, project management, or equivalent experience preferred
  • Business intelligence applications and/or cloud services (e.g., Alteryx, Domo, Looker, PowerBI, Qlik, Tableau, SiSense).
  • No/Low code ETL and Orchestration platforms such as AWS Glue DataBrew or SAS.
  • Data querying, transformation, and processing languages such as SQL, Python, Power BI DAX/M Query, or SAS.
  • Operational, enterprise, and analytical relational/non-relational data stores such as PaaS cloud technologies, SQL Server, Oracle, and DB2.
  • Knowledge of cloud platforms and surrounding ecosystems (AWS, Azure).

Responsibilities

  • Build and maintain metrics, reports, and dashboards (e.g., Power BI) to support key business decisions.
  • Create visualizations to scale operations and measure success.
  • Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems
  • Creating new and better ways to lead the business toward success
  • Actively seeking opportunities to learn via experimentation using both successes and failures to build future skills
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