Senior Data Analyst

Envoy•San Francisco, CA

6d•$162,500 - $176,250•Onsite

About The Position

Envoy is hiring a hands-on, technical Senior Data Analyst to determine which marketing motions create pipeline and revenue and to build the data foundation needed for reliable measurement. The analyst will partner with Marketing and RevOps leadership while building and shipping production data products with the Data team. This is an onsite role requiring at least five days a week, Monday through Friday, at Envoy’s San Francisco headquarters. The role owns the marketing data platform and pipelines on AWS, builds tested dbt funnel and semantic models, develops multi-touch attribution, and creates internal tools and serverless data APIs. It also connects warehouse data back to Salesforce, Marketo, and Outreach; joins product events with CRM records; develops and deploys lead-scoring and intent models; and supports experimentation and spend measurement. The analyst is responsible for reliability, including data freshness, testing, CI, cost, and monitoring, and can use AI tooling to accelerate work while verifying its output.

Requirements

At least 5 years of experience in analytics, including at least 2 years focused on marketing, growth, or go-to-market analytics at a B2B SaaS company.
Expert SQL skills, including window functions, CTE-first style, query performance on a columnar warehouse, and the ability to identify fan-out joins.
Production dbt experience building layered models, writing tests, and shipping through code review and CI.
Fluency in Python for analysis and statistics, including pandas, statsmodels, or similar, as well as experiment design and causal inference basics.
Deep familiarity with marketing and sales data, including Salesforce leads, contacts, campaigns, and opportunities; marketing automation such as Marketo or HubSpot; and web and product event tracking such as Segment or similar.
Working knowledge of attribution approaches and their trade-offs, funnel analysis, and cohort analysis.
Comfort with Git, pull requests, and the command line.
Ability to influence decisions and clearly explain and defend model assumptions to senior stakeholders.

Nice To Haves

Experience with Airflow or another orchestrator and ingestion tools such as Fivetran or Estuary.
Semantic layer or metrics-layer experience, such as dbt Semantic Layer, Omni, or Looker.
Data lake experience with Iceberg, Spectrum, Athena, or Spark.
Experience with lead scoring, propensity modeling, or marketing-mix modeling.
Exposure to sales engagement and revenue intelligence data, such as Outreach or Gong, or job-change signals.
Experience using LLMs and AI tools to accelerate analytics work, with rigor in checking their output.

Responsibilities

Own and operate marketing data pipelines on AWS using Redshift, S3, Glue, Lambda, and Iceberg, with infrastructure managed in Terraform and deployed through GitHub Actions.
Build tested dbt staging, intermediate, mart, and semantic models for the lead-to-revenue funnel.
Design and operate multi-touch attribution across web, paid, events, email, and outbound; schedule jobs in Airflow and expose results through governed tables and APIs.
Build lightweight internal marketing applications, such as campaign ROI explorers, lead-routing consoles, and self-service query apps, using AWS Amplify, Lambda, API Gateway, and authenticated access.
Build serverless data APIs that expose warehouse metrics to applications, Slack, and AI agents through Lambda-backed endpoints and MCP servers, using least-privilege IAM.
Use reverse ETL to sync scores, segments, and product signals from the warehouse into Salesforce, Marketo, and Outreach.
Join Segment product events in S3/Iceberg with CRM records to measure product-qualified leads and product-led growth conversion.
Develop lead-scoring and intent models using product usage, firmographic, and job-change signals; deploy and monitor them in production.
Design A/B and holdout experiments and report customer acquisition cost, payback, and channel ROI with Finance.
Use Claude Code and LLM tooling to prototype applications, generate models, and automate analysis, while verifying outputs.
Maintain source freshness, data tests, CI checks, cost controls, and monitoring to ensure pipeline reliability.
Partner with Marketing and RevOps leadership as an analytical partner and explain and defend analytical model assumptions.