Director of Data & Analytics Engineering

Posted 2 months agoViewed
United States, CanadaFull-TimeFintech, Lending
Company:Reach Financial
Location:United States, Canada, EST
Languages:English
Seniority level:Director, 10+ years
Experience:10+ years
Skills:
LeadershipPythonSQLArtificial IntelligenceBusiness IntelligenceCloud ComputingData AnalysisMachine LearningSnowflakeCross-functional Team LeadershipTableauData engineeringMentoringData visualizationData modeling
Requirements:
10+ years of experience in data and analytics. At least 5 years leading high-performing cross-functional teams. Deep understanding of modern data architecture, governance, and analytics within a regulated industry (fintech, lending, or financial services). Proven track record of scaling data teams and platforms in high-growth environments. Strong communicator who can simplify complex data concepts. Experienced in operationalizing data governance, self-service analytics, and data products. Strategic thinker who can balance long-term vision with hands-on execution. Forward-looking leader with curiosity about AI, automation, and emerging data technologies.
Responsibilities:
Define and execute a clear data and analytics strategy aligned with business goals. Partner with senior leadership across departments to embed data into key decisions. Establish a unified architecture and measurement framework. Anticipate trends in data and AI for high-impact innovations. Drive transformation toward a cloud-first, scalable, governed data platform. Lead the evolution of the modern data stack (Fivetran, dbt, Snowflake, Tableau). Implement best practices for ingestion, transformation, observability, and data quality. Champion strong data governance, lineage, and discoverability. Develop a unified analytics layer for self-service reporting and executive decision-making. Ensure business teams can independently access accurate, timely insights. Translate complex data into clear narratives. Ensure data product ownership principles. Partner with the Data Science team to evolve ML models. Collaborate to expand AI/ML use cases. Help establish a scalable, explainable ML Ops practice. Define processes for rapid experimentation. Establish clear SLAs, data quality standards, and cost controls. Embed data observability and incident management. Continuously improve internal processes. Build, mentor, and scale a high-performing data team. Foster a culture of accountability, curiosity, and innovation. Develop leadership depth and support cross-functional collaboration. Represent Reach’s data capabilities internally and externally.
About the Company
Reach Financial
51-100 employeesFinancial Services
View Company Profile
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