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We are seeking a Senior Business Execution Consultant to join the Branch Distribution Strategy & Operations team in a hybrid strategy, analytics, and data engineering role. This position is ideal for someone who enjoys shaping business strategy while also building the data foundations required to deliver it. In this role, you will influence the retail distribution strategy for one of the nation’s largest banks. You will uncover insights that drive executive decisions and ensure we maintain a robust, well governed data environment. If you excel at translating technical solutions into business outcomes—and have experience designing or improving production grade data pipelines—we would love to hear from you. This position shapes decisions, not just datasets. You will own the full path from problem framing → data engineering → analysis → insight → recommendation → measured business impact. You will pair hypothesis driven strategic thinking and executive storytelling with hands-on SQL/Python and modern data tooling to build clean, scalable datasets and decision grade analytics. This role has a significant data engineering component. You will help define and implement data architecture standards, optimize pipelines, enhance data quality, and ensure the data powering our decision-making is durable, trustworthy, and ready for scaled use. This is not a pure data engineering or pure reporting role. We are looking for a strategist who can dig into the data, engineer the right solutions, and use them to drive meaningful business decisions.
Job Responsibility:
Translate ambiguous business questions into structured hypotheses and learning agendas
Craft concise, executive-ready narratives with options, tradeoffs, and a recommended path
influence decisions involving Branch Distribution Strategy
Define simple, durable metric frameworks/OKRs
align leaders on what “good” looks like and how we’ll measure it
Partner with stakeholders to convert insights into strategy, roadmaps, and pilots—then track outcomes
Write production-grade SQL and solid Python to build reusable datasets, enable self serve analytics, and automate recurring workflows
Improve data quality, lineage, and documentation
help shape data contracts with upstream teams
Publish decision dashboards (Power BI) with unambiguous definitions and business context
Adhere to data governance/model risk expectations
document methods, assumptions, and limitations
Requirements:
4+ years of Business Execution, Implementation, or Strategic Planning experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
4+ years of Advanced data analytics or statistical modeling
Nice to have:
3+ years of consulting experience
3+ years of banking, financial services, or Branch Distribution Strategy experience
Strong proficiency in SQL (production grade) and Python for data transformation, automation, and analytical workflows
Experience designing, building, and maintaining reusable datasets, data models, or pipelines
Demonstrated ability to improve data quality, lineage, metadata, and documentation, including partnership with upstream teams to shape data contracts
Experience developing decision grade analyses such as customer segmentation, funnel analysis, attribution/driver models, uplift/propensity modeling, or unit economics
Strong ability to translate ambiguous business questions into structured hypotheses, analytical plans, and actionable insights
Exceptional executive communication skills, with a track record of crafting clear narratives, options/tradeoffs, and recommended strategic paths
Experience defining and operationalizing metric frameworks/OKRs and ensuring consistent definitions across stakeholders
Experience with GIS analytics
Proficiency building Power BI dashboards or similar BI tools with clear logic, definitions, and business context
Familiarity with data governance, model documentation, risk controls, and sound methodological practices
Ability to operate in fastmoving, ambiguous environments
comfortable executing end-to-end from problem framing → data engineering → analysis → insight → recommendation → measured impact
Experience influencing cross functional partners (e.g., Product, Finance, Risk) and converting insight into strategy, roadmaps, or pilots