Senior Manager, Senior Analytics Engineer

Qcells invites an experienced analytics engineering leader to serve as the Senior Manager, Senior Analytics Engineer. This role acts as the technical and architectural owner of the enterprise data platform within EnFin’s environment, overseeing the end-to-end data pipeline from source systems through the reporting layer. The position centers on the semantic data layer, hands-on data transformation into governed, analysis-ready models, and the ongoing establishment of platform standards, data quality, and observability to mature the BI and analytics capabilities.

Responsibilities

• Design, implement, document, and maintain production-grade data models using dbt Core or dbt Cloud, converting raw sources from Salesforce, Q2, and related pipelines into clean, tested, analysis-ready tables and views in Snowflake.
• Lead the design and governance of the enterprise semantic layer, defining naming conventions, modeling standards, and documentation to position the data warehouse as the single authoritative source for all business reporting and analysis.
• Create analytics-ready intermediate data layers to support various functional areas across the enterprise as needed.
• Develop and maintain a comprehensive testing framework including schema tests, data freshness checks, and statistical anomaly detection to protect data integrity; implement alerting standards and on-call protocols for pipeline failures.
• Serve as the enterprise authority on data definitions, metrics governance, and analytical tool standards; manage the certified metrics layer in Tableau and Snowflake to ensure consistent calculations and definitional integrity across surfaces and units.
• 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 business users and aligned with business purposes.
• Translate business-driven data quality rules into validation logic covering completeness, conformance, sequencing, and referential integrity.
• Conduct baseline data quality assessments and generate scorecards by domain and field.
• Own and deliver an executive-level data quality observability dashboard, providing real-time transparency into pipeline health, field completeness, and data quality SLAs across key 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, understanding field mapping, sync frequencies, and change data capture status for critical fields.
• Evaluate and implement solutions to address field history gaps as appropriate.
• Govern 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 with the technology team.
• Mentor analytics engineers and BI analysts, providing technical direction, code reviews, and career growth support to elevate the broader BI function.
• Advance the analytics engineering practice by establishing coding standards, pull request workflows, CI/CD integration for dbt, and scalable documentation expectations.
• 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.

Requirements

• Bachelor’s degree in a quantitative or technical field; advanced degree is a plus. A 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 role owning a production data platform.
• Strong SQL proficiency with demonstrated ability to write, debug, and optimize complex queries.
• Proficient in Python for automating data analysis, developing testing scripts, and supporting data visualization needs.
• Hands-on experience with Git or equivalent version control systems.
• Practical 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 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 comfort working with 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

Location

Teaneck, NJ (onsite)

Education

Bachelor’s degree

Physical, Mental and Environmental Demands

• Mobility and endurance requirements aligned with typical office and data operations roles
• Standing: 20 percent of time; Sitting: 70 percent of time; Walking: 10 percent of time
• Strength: Pulling up to 10 pounds; other lifting or strenuous activities are not specified
• Dexterity: Typing frequently; handling and reaching required to varying degrees
• Agility: Turning, twisting, and bending occur intermittently
• Climbing, crawling, and kneeling are not indicated as routine requirements