Learn more at envoy.com

About the role

Marketing at Envoy runs on a mix of product-led signups, inbound demand and outbound motions across several products. You'll be the person who tells us which of those actually create pipeline and revenue — and you'll build the data foundation that makes the answer trustworthy.

This is a hands-on, technical role. You'll write production SQL and dbt models, not just query tables someone else built. You'll sit with Marketing and RevOps leadership as their analytical partner, and with the Data team as a builder who ships to the warehouse.

This is an onsite position that requires at least 5 days a week (Monday - Friday) in our San Francisco HQ office.

You will

You'll work like a forward-deployed engineer embedded in Marketing: sit with the team, find the problem, and ship the data product that solves it — on AWS, in production, end to end.

• Own the marketing data platform on AWS. Build and run marketing pipelines across Redshift, S3, Glue, Lambda and Iceberg tables, with infrastructure defined in Terraform and deployed through GitHub Actions.

• Model the funnel in dbt. Ship tested staging, intermediate and mart models for lead → MQL → SQL → opportunity → closed-won, plus semantic models others self-serve from.

• Build attribution as a product. Design multi-touch attribution across web, paid, events, email and outbound, run it as scheduled jobs in Airflow, and expose it through governed tables and APIs.

• Ship internal apps, not just dashboards. Build lightweight tools Marketing uses daily — campaign ROI explorers, lead-routing consoles, self-serve query apps — on AWS Amplify, Lambda and API Gateway with Cognito/Okta auth.

• Build serverless data APIs. Expose warehouse metrics to apps, Slack and AI agents through Lambda-backed endpoints and MCP servers, with least-privilege IAM.

• Close the loop with reverse ETL. Sync scores, segments and product signals from the warehouse back into Salesforce, Marketo and Outreach so Sales and Marketing act on them.

• Engineer the event lake. Join Segment product events in S3/Iceberg to CRM records to measure product-qualified leads and PLG conversion at scale.

• Build lead scoring and intent models. Turn product usage, firmographic and job-change signals into scores in Python or Databricks, then deploy and monitor them in production.

• Run experiments and measure spend. Design A/B and holdout tests, and report CAC, payback and channel ROI with Finance.

• Use AI to move faster. Build with Claude Code and LLM tooling to prototype apps, generate models and automate analysis, with the rigor to verify the output.

• Keep it reliable. Own source freshness, data tests, CI checks, cost and monitoring so a broken pipeline never reaches a board deck.

You have

• 5+ years in analytics, with at least 2 focused on marketing, growth or go-to-market analytics at a B2B SaaS company.

• Expert SQL: window functions, CTE-first style, query performance on a columnar warehouse (Redshift, Snowflake or BigQuery), and the judgment to spot a fan-out join before it reaches a dashboard.

• Production dbt experience: you've built layered models, written tests, and shipped through code review and CI.

• Fluency in Python for analysis and statistics (pandas, statsmodels or similar), including experiment design and causal inference basics.

• Deep familiarity with marketing and sales data: Salesforce objects (leads, contacts, campaigns, opportunities), marketing automation (Marketo or HubSpot), web and product event tracking (Segment or similar).

• Working knowledge of attribution approaches and their trade-offs, plus funnel and cohort analysis.

• Comfort with Git, pull requests and the command line.

• A track record of influencing decisions: you can explain a model's assumptions to a CMO in two sentences and defend them under questioning.

Nice to have

• Experience with Airflow or another orchestrator, and with ingestion tools such as Fivetran or Estuary.

• Semantic layer or metrics-layer experience (dbt Semantic Layer, Omni, Looker).

• Data lake experience: Iceberg, Spectrum, Athena or Spark.

• Lead scoring, propensity or marketing-mix modeling.

• Exposure to sales engagement and revenue intelligence data (Outreach, Gong) or job-change signals.

• Experience using LLMs and AI tools to speed up analytics work, with the rigor to check their output.