Senior Manager, Senior Analytics Engineer

Senior analytics engineering leader at Qcells in Teaneck, NJ onsite, accountable for the enterprise data platform from ingestion to reporting, with emphasis on governance, the semantic layer, and analytics maturity.

Responsibilities

• Architect, build, maintain, and document production-grade data models using dbt Core or dbt Cloud, transforming raw data from Salesforce and Q2 into clean, tested, analysis-ready tables and views in Snowflake.
• Own the design and governance of the enterprise semantic layer, establishing naming conventions, modeling standards, and documentation practices to ensure the data warehouse is the single source of truth for reporting and analysis.
• Develop analytics-ready intermediate data layers to support functional areas across the enterprise as needed.
• Create and maintain a comprehensive testing framework, including schema tests, data freshness checks, and anomaly detection; establish alerting standards and on-call protocols for pipeline failures.
• Serve as the enterprise authority on data definitions, metrics governance, and analytical tool standards; own the certified metrics layer in Tableau and Snowflake with consistent calculation logic across surfaces.
• Collaborate with third-party vendors to enable rapid implementation and adoption of new analytical or AI tools.
• Ensure metric definitions are accessible to non-technical users and aligned with business needs.
• Translate business-defined data quality rules into validation logic covering 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 with real-time insight into pipeline health, field-level completeness, and data quality SLAs across critical domains.
• Investigate data anomalies, trace issues to their source, and coordinate remediation with the technology team.
• Partner with the technology team on enterprise ETL pipelines, including field mapping, sync frequencies, and change data capture status for key business fields.
• Evaluate and implement solutions to address field history gaps as appropriate.
• Oversee governance of the Snowflake analytics environment, including schema organization, role-based access controls, object lifecycle policies, and published best practices; collaborate on warehouse cost optimization and compute management.
• Mentor analytics engineers and BI analysts, provide technical direction and code reviews, and support career growth to elevate the BI function.
• Advance the analytics engineering practice by setting coding standards, pull request workflows, CI/CD integration for dbt, and scalable documentation expectations.
• Contribute to hiring and onboarding decisions for analytics engineering roles and partner with the Director to shape the long-term technical roadmap for the BI and analytics platform.

Requirements

• Bachelor's degree in a quantitative or technical field; advanced degree a plus. Minimum 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 role owning a production data platform.
• Strong SQL proficiency with the ability to write, debug, and optimize complex queries.
• Strong Python proficiency to automate data analysis, develop testing scripts, and support data visualization needs.
• Hands-on experience with Git or other version control systems.
• Hands-on experience with Snowflake or a comparable cloud data warehouse, including schema design, query optimization, and RBAC. Direct experience with dbt (Core or Cloud) is required.
• Proven ability to build data pipelines or models that are well-documented, tested, and maintainable by others.
• Strong written and verbal communication skills, with the ability to engage both technical and non-technical stakeholders.
• Ability to thrive in a fast-paced, dynamic environment, manage competing priorities, and deliver results under tight timelines.

Technologies

• dbt Core
• dbt Cloud
• Snowflake
• Salesforce
• Q2
• Tableau
• Python
• SQL
• Git

Physical, Mental & Environmental Demands

• Primarily sedentary work with approximate time allocations: sitting about 70 percent, standing about 20 percent, and walking about 10 percent.
• Manual tasks include keyboard use; lifting up to 10 pounds as needed.
• Repetitive movements such as typing are common; occasional bending, reaching, and turning may be required.
• On-site work in Teaneck, NJ within a standard office environment.