About Turn/River

Turn/River Capital is a private equity firm that partners with software businesses to drive AI transformation and catalyze capital-efficient growth. As a team of equal parts software operators and investors, we have the people, data-driven playbooks and flexible capital to re-architect engineering, product, go-to-market, talent and resourcing to build enduring value. Founded in 2012 and based in San Francisco, Turn/River has $5.6bn in committed capital and invests globally with a focus on North America and Europe.

About the Role

We’re looking for a Senior Manager, Data Engineering to design, build, and scale the data infrastructure that powers analytics across Turn/River’s portfolio.

You’ll lead the evolution of our data platform, ensuring our systems are connected, reliable, and ready for AI-driven insight generation.

Your near-term focus will be strengthening marketing data pipelines (Ads, GA, HubSpot, Salesforce), while your long-term remit spans the entire data org: defining standards, improving scalability, and enabling data-driven execution across all functions.

This role is highly collaborative within Turn/River and across our portfolio companies. You’ll partner closely with Marketing, RevOps, and Analytics leaders to build a unified, AI-ready data foundation that supports marketing analytics today and scales to all go-to-market functions over time.

Key Responsibilities

Data Platform Ownership: Lead the design, maintenance, and scalability of the portfolio’s BigQuery data platform and ETL/ELT pipelines.

Marketing Data Integration: Partner with Marketing, RevOps, and Analytics Leads to unify GTM systems (Ads, GA, HubSpot, Salesforce) into reliable revenue-linked funnel models.

Data Architecture & Modeling: Build standardized schemas and dbt packages to support analytics consistency across dozens of companies.

Quality & Governance: Implement CI/CD, data validation, and tagging standards that ensure accuracy and trust.

AI Workflows: Design and implement LLM-powered workflows (prompt engineering, RAG, agent-based automation) to streamline content generation, campaign optimization, and performance analysis.

Team Leadership: Mentor junior data engineers and analysts, foster engineering best practices, and lead knowledge-sharing across the portfolio.

Platform Roadmap: Define and execute a 12–24-month roadmap evolving the platform toward near-real-time flows, identity resolution, and modular reuse.

Cross-Functional Collaboration: Partner with Analytics and Systems teams to turn fragmented data into actionable insight and scalable automation.

Qualifications

5+ years of data or analytics engineering experience, including ownership of production-grade ELT/ETL systems.

Deep expertise in SQL and Python, with hands-on experience using dbt, Fivetran, Airbyte, or Matillion.

Advanced proficiency in BigQuery (preferred) or Snowflake and modern cloud-based architecture.

Proven ability to design scalable data models and enforce engineering best practices, including Git-based workflows and CI/CD automation.

Experience integrating GTM systems (Ads, HubSpot, Salesforce, GA4) and building reliable revenue-linked pipelines.

Demonstrated success leading complex data projects and mentoring technical contributors.

Exceptional communicator who can bridge technical detail with business context and cross-functional impact.

Bachelor’s degree in a quantitative field (Computer Science, Engineering, Statistics, Mathematics, or Business Analytics) or equivalent professional experience.

Intellectually curious, systems-minded, and detail-obsessed — passionate about building scalable, reusable, and future-proof data infrastructure that drives portfolio growth.

Location

San Francisco, hybrid work model.

Compensation

The annual base salary range for this role is $227,500 - $237,500, taking into account numerous variable factors that are considered in making compensation decisions including but not limited to skill sets, experience, training, licensure, certifications, and organizational requirements.