Senior Lead Analytics Consultant - Data Engineering & Analytics

Job description

Senior Lead Analytics Consultant -Data Engineering \& Analytics About this role: Wells Fargo is seeking a Senior Lead Analytics Consultant. In this role, you will: Act as an advisor to senior leadership to develop or influence analytical strategies for highly complex business and technical needs Lead the strategy and resolution of highly complex and unique challenges requiring analytical application of industry techniques and advanced data from multiple sources across the enterprise Deliver solutions that are long-term, large-scale, and require vision, innovation, and coordination of highly complex activities Provide vision, direction, and expertise to senior leadership on implementing innovative and significant business solutions that align to the enterprise Ensure adherence to compliance and legal regulations and policies on all projects managed Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership Required Qualifications: 7 years of Analytics, Reporting, Financial Modeling or Statistics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education Desired Qualifications: 7 years of experience to serve as a senior individual contributor driving advanced analytics, data science, and modern data strategy across complex enterprise environments. This role is positioned for a highly autonomous analytics leader who can operate across data engineering, data science, and business analytics domains connecting disparate data sources, ensuring data trustworthiness, and generating actionable insights that improve people, processes, and technology outcomes. This is not a traditional reporting or dashboard development role. The focus is on exploratory, predictive, and prescriptive analytics, combined with modern data architecture, data quality, and lineage practices. Lead advanced analytics initiatives to identify trends, risks, opportunities, performance gaps, and improvement areas across business and technology functions. Proactively analyze and integrate data from multiple systems, platforms, and business domains to generate trusted and actionable insights. Apply statistical analysis, predictive modeling, anomaly detection, machine learning, and advanced analytical techniques to solve complex business problems. Partner with data engineering, business intelligence, product, technology, risk, compliance, audit, and business stakeholders to define analytics needs and deliver meaningful outcomes. Define, govern, and enhance enterprise metrics, key performance indicators, key risk indicators, scorecards, measurement frameworks, and executive reporting routines. Drive data modernization initiatives by supporting the transition from legacy reporting and analytics solutions to modern, cloud-based, scalable data ecosystems. Establish and influence data quality strategies, metadata practices, lineage visibility, data governance standards, and data management practices. Work across the full data lifecycle, including data extraction, integration, transformation, modeling, validation, analysis, visualization, and insight generation. Leverage tools and platforms such as SQL, Python, Power BI, Alteryx, Databricks, PySpark, SAS, Teradata, Google Cloud Platform, BigQuery, and modern distributed compute platforms. Translate complex analytics into clear business recommendations, executive narratives, governance updates, and decision-ready insights. Present insights, recommendations, trade-offs, and analytical findings to senior leaders, executives, regulators, audit partners, governance committees, and cross-functional stakeholders. Mentor analytics professionals, establish analytical best practices, and influence standards for analytics delivery, documentation, quality, and stakeholder communication. Navigate ambiguity, independently identify opportunities, drive clarity, and deliver results in a fast-paced enterprise environment Job Expectations: 7 years of experience in analytics, data science, business intelligence, data engineering, data management, enterprise reporting, risk analytics, or a related analytical function. Advanced proficiency in SQL, Python, Power BI, and Alteryx, with the ability to analyze large, complex datasets and develop actionable insights. Strong experience in statistical analysis, predictive modeling, machine learning, anomaly detection, segmentation, clustering, regression, decision trees, random forests, and related advanced analytical methods. Experience with modern data platforms such as Databricks or equivalent distributed compute and cloud-based analytics platforms. Strong understanding of data engineering, data modeling, data warehousing, enterprise data marts, dimensional modeling, data pipelines, and distributed data processing. Experience with data integration patterns, including ETL, ELT, and streaming concepts. Strong understanding of data governance, data quality, metadata, lineage, data management, and data as a product concepts. Demonstrated ability to translate analytical findings into business recommendations, measurable outcomes, and executive-ready insights. Excellent communication, stakeholder management, consulting, influencing, facilitation, and storytelling skills. Ability to work independently, collaborate across technical and non-technical teams, navigate ambiguity, and deliver results in a complex enterprise environment. Proven experience with data modernization, Lakehouse architecture, modern data platforms, data quality, metadata, lineage, and enterprise data governance. Hands-on experience with tools such as PySpark, SAS, R, Teradata, Google Cloud Platform, BigQuery, Databricks, Power BI, Alteryx, or similar analytics platforms. Experience defining enterprise metrics, KPIs, KRIs, scorecards, measurement frameworks, executive reporting, and governance routines. Ability to identify emerging risks, ass