Senior Manager, Senior Analytics Engineer

POSITION DESCRIPTION

Reporting to EnFin’s Director of Business Intelligence & Analytics Engineering, the Sr. Manager, Senior Analytics Engineer is the senior technical and architectural owner of EnFin’s enterprise data platform responsible for the design, governance, and execution of the full data pipeline from source systems through the reporting layer. You will serve as the principal architect of our semantic data layer, leading the hands-on technical work that transforms raw data from our CRM systems, decision engine, and servicing pipelines into clean, governed, and rigorously documented data models that the enterprise can trust and act on at scale. As EnFin’s analytics engineering function continues to mature, this role is expected to establish and enforce platform standards, drive continuous improvement in data quality and observability, and develop the technical capabilities of the broader BI and analytics team. You will work closely with senior stakeholders and lead cross-functional engagement across Technology, Risk Management, Operations, Servicing, Capital Markets, Finance, and Compliance. This position will be based out of one of our offices in Irvine, CA, San Francisco, CA, Santa Clara, CA, Teaneck, NJ with an in-office schedule set at the direct manager’s discretion in accordance with the company policies and procedures. Remote may be considered in exceptional cases.

RESPONSIBILITIES

Data Modeling and Transformation — Architect, build, maintain, and document production-grade data models in dbt Core or dbt Cloud, transforming raw source data from Salesforce, Q2, and servicing pipelines into clean, tested, analysis-ready tables and views within Snowflake. Own the design and governance of EnFin’s enterprise semantic layer, establishing naming conventions, modeling standards, and documentation practices that ensure the data warehouse serves as the single authoritative source of truth for all business reporting and analysis. Build analytics-ready intermediate data layers as needed to support functional areas of the enterprise. Design and maintain a comprehensive testing framework including schema tests, data freshness checks, and statistical anomaly detection to ensure data integrity and catch regressions before they reach business users; establish alerting standards and on-call protocols for pipeline failures. Analytics and AI Tool Integration — Serve as the enterprise authority on data definitions, metrics governance, and analytical tool standards. Own the certified metrics layer in Tableau and Snowflake, ensuring consistent calculation logic and definitional integrity across all reporting surfaces and business units. Work closely with third party vendors to ensure rapid implementation and adoption of new analytical or AI tools. Ensure that metric definitions are accessible to non-technical business users and relevant for their business purposes. Data Quality and Validation — Translate business-defined data quality rules into validation logic including data completeness, conformance, sequencing, and referential integrity. Run baseline data quality assessments and produce scorecards by domain and field. Own and deliver an executive-level data quality observability dashboard, providing real-time transparency into pipeline health, field-level completeness, and data quality SLAs across all critical business domains. Investigate data anomalies, trace issues to their source, and coordinate remediation with the technology team. Pipeline and Infrastructure Collaboration — Partner with the technology team on enterprise ETL pipelines, understanding field mapping, sync frequencies, and change data capture status for all critical business fields. Evaluate and implement solutions to solve field history gaps as appropriate. Own the governance of the Snowflake analytics environment, including schema organization, role-based access controls, object lifecycle policies, and published best practices for analyst-built objects. Partner with the technology team on warehouse cost optimization and compute management. Team Development and Analytics Practice Leadership — Mentor and develop analytics engineers and business intelligence analysts on the team, providing technical direction, code review, and career growth support. Act as a force multiplier by elevating the technical capabilities of the broader BI function. Drive the analytics engineering practice forward by establishing coding standards, pull request workflows, CI/CD integration for dbt, and documentation expectations that scale as the team grows. Contribute to hiring and onboarding decisions for analytics engineering roles. Partner with the Director to shape the long-term technical roadmap for the BI and analytics platform.

REQUIRED QUALIFICATIONS

Bachelor’s degree in a quantitative or technical field; advanced degree a plus. Minimum of 8+ years of professional experience, including 5+ years in analytics engineering, data engineering, or business intelligence, with at least 2+ years in a senior individual contributor or technical lead capacity owning a production data platform. Strong SQL proficiency and ability to write, debug, and optimize complex queries. Strong Python proficiency and ability to automate data analysis, develop testing scripts, and support data visualization needs. Hands-on experience with git or other comparable version control systems. Hands-on experience with Snowflake or a comparable cloud data warehouse, including schema design, query optimization, and role-based access control. Direct experience with dbt (Core or Cloud) required. Demonstrated ability to build data pipelines or models that are well-documented, tested, and maintainable by others. Strong written and verbal communication skills, including the ability to work comfortably with both technical and non-technical stakeholders. Ability to work in a fast-paced, dynamic environment, manage competing priorities, and deliver results under tight timelines.

PREFERRED QUALIFICATIONS

Direct experience with Tableau, including building certified data sources, managing extract schedules, and governing self-service analytics at an enterprise scale. Domain experience in consumer finance, solar, or fintech with working knowledge of loan origination, underwriting decisioning, or servicing data models. Experience with Salesforce data architecture, including standard and custom object relationships, reporting snapshots, and strategies for extracting historical data via CDC or batch processes. Proficiency with AI-assisted development workflows and demonstrated ability to evaluate, pilot, and integrate emerging analytics or AI tooling into a production data environment. Hanwha Q CELLS America Inc. ("HQCA") is a Qcells company, one of the world’s largest manufacturers and providers of solar photovoltaic (PV) products and solutions. Headquartered in Irvine, California, HQCA has been rapidly expanding its business in North America through the expansion of products and solutions, including distributed energy solutions, direct-to-homeowner solar sales and financing, and EPC services. We provide an opportunity to be part of an exciting and growing world-class global business in an interesting and expanding industry of the future. #J-18808-Ljbffr Qcells