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Procore Technologies, Inc.

Senior Manager, Analytics Engineering & Data Science

TX, United States

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Job details

Experience: 10+ years of experience in data engineering, analytics engineering, or data science, including 4+ years leading and coaching teams of engineers or scientists.
Posting language: English
Posted: Nov 13, 2026

Original job description

Procore Technologies is the leading technology partner for every stage of construction. We empower construction teams to drive efficiency and mitigate risk through actionable AI & data-driven insights. For over 20 years, we have been transforming the industry with purpose-built solutions for construction professionals and we’re looking for talented people to help us build together.

We are looking for a Senior Manager, Analytics Engineering & Data Science to shape how we understand our customers and the products they rely on every day by building models, experiments, and AI-enabled tooling that turn product data into actionable business decisions. As the Senior Manager, Analytics Engineering & Data Science on our Product & Technology team, you will work closely with Data and AI Engineering, product teams, and cross-functional data organizations to deliver rigorous insights on customer adoption and platform value. Your strengths in analytics engineering, applied data science, and team leadership in an AI-first environment will drive your success.

You will report to the VP, Engineering and will be based in Austin, TX.

What You’ll Do

Lead, coach, and scale a distributed team of analytics engineers, data scientists, and researchers, advancing technical rigor, statistical depth, and context engineering capabilities within an AI-first operating model.
Drive a proactive insight agenda for product and engineering leadership, delivering deep-dive analyses into adoption, user friction, and value drivers that directly shape product roadmaps and strategic investments.
Measure the business impact of feature rollouts, customer programs, and platform AI features using robust experimentation and causal inference techniques to optimize customer value and cost-to-serve.
Deliver clear, executive-ready recommendations backed by empirical data to product, engineering, and customer success leadership, tracking downstream business outcomes.
Develop and deploy production machine learning models for customer health scoring, cost forecasting, predicted time-to-value, and entity intelligence that link disparate records into unified identities.
Own and govern the semantic layer for product and customer analytics as a versioned product, maintaining certified datasets, metric definitions, and metadata context required by AI agents.
Build and evaluate agentic analytics workflows (such as Genie spaces, Claude skills, and MCP tools) using golden test sets, accuracy monitoring, and regression testing, while partnering with engineering on telemetry and tracking quality.

What You Bring

10+ years of experience in data engineering, analytics engineering, or data science, including 4+ years leading and coaching teams of engineers or scientists.
Hands-on expertise designing, building, and operating production data pipelines and models at scale using SQL and Python, including data orchestration, data quality testing, and event instrumentation.
Strong familiarity with modern data lakehouse architectures; Databricks experience (Spark, Delta Lake, Unity Catalog) strongly preferred, with value placed on Snowflake and dbt.
Proven track record delivering high-impact product insights that influenced executive roadmaps and technical priorities.
Experience owning semantic layers, data governance, and metadata architecture to enable AI agents and self-serve business analytics with lineage and access controls.
Practical expertise applying experimentation, causal inference, and production machine learning (such as predictive scoring, classification, and entity resolution) to product datasets.
Demonstrated experience operationalizing and measuring AI-assisted analytics workflows in production environments.
Bachelor’s degree in computer science, statistics, economics, or a related quantitative field, or equivalent practical experience.

Strong-fit Background Might Include

Data Science Manager or Lead Analytics Engineer at a high-growth SaaS enterprise
Machine Learning / Product Analytics Engineering Lead transitioning to senior people leadership
Enterprise Data Architect or Semantic Layer Lead supporting product engineering organizations
Quantitative Analytics Lead with deep experience in AI tooling, causal inference, and data modeling

Discover our recruiting process and interview tips from our talent acquisition team on our #LifeAtProcore blog.

Additional Information

Base Pay Range:

198,240.00 - 272,580.00 USD Annual This role may also be eligible for Equity Compensation and/or Bonus Incentive Compensation. Procore is committed to offering competitive, fair, and commensurate compensation. Actual compensation will be based on a candidate’s job-related skills, experience, education or training, and location.

For Los Angeles County (unincorporated) Candidates:

Procore will consider for employment all qualified applicants, including those with arrest or conviction records, in accordance with the requirements of applicable federal, state, and local laws, including the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act.

A criminal history may have a direct, adverse, and negative relationship on the following job duties, potentially resulting in the withdrawal of the conditional offer of employment: 1. appropriately managing, accessing, and handling confidential information including proprietary and trade secret information, as well as accessing Procore's information technology systems and platforms; 2. interacting with and occasionally having unsupervised contact with internal/external customers, stakeholders, and/or colleagues; and 3. exercising sound judgment.

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Jobs in the United States