{"id":3557567,"url":"https://alion.io/job/envoy-senior-data-analyst","title":"Senior Data Analyst","company":{"id":171789,"name":"Envoy","domain":"envoy.com","url":"https://alion.io/company/envoy-3","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":null},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":162500,"max":176250,"currency":"USD","period":"year","gross":null,"usd_annual":176250},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"Claude Code","optional":false},{"name":"Databricks","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Google BigQuery","optional":false},{"name":"IAM","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Okta","optional":false},{"name":"Python","optional":false},{"name":"Slack","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Statsmodels","optional":false},{"name":"Terraform","optional":false},{"name":"Fivetran","optional":true},{"name":"Looker","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-10-30T23:01:04Z","employer_posted_date":"2026-10-30","last_verified_at":"2026-11-01T07:29:56Z","board_verified":true,"closed_at":null,"days_open":1,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":1},"description":"Envoy protects the places the world relies on most by unifying people, spaces, and communications in one secure, integrated workplace management platform and ecosystem. More than 16,000 workplaces around the world trust Envoy to run secure, compliant, and connected operations across every location.\nFrom manufacturing sites and data centers to pharmaceutical labs, healthcare facilities, and corporate headquarters, Envoy makes safety, security and associated regulatory compliance something our customers can be proud of. The industry is littered with box-checking and arcane manual processes as the standard and we see our role as the company to bring experience, efficiency and simplicity everywhere. Great experiences bring great security - it's a requirement. We hope this problem space excites you and we hope you're ready to come sweat the details with us - we have a lot of customers counting on us! And it certainly won't be easy.\nLearn more at envoy.com\nAbout the role\nMarketing 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.\nThis 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.\n This is an onsite position that requires at least 5 days a week (Monday - Friday) in our San Francisco HQ office.\nYou will\nYou'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.\nOwn 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.\n\nModel 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.\n\nBuild 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.\n\nShip 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.\n\nBuild serverless data APIs. Expose warehouse metrics to apps, Slack and AI agents through Lambda-backed endpoints and MCP servers, with least-privilege IAM.\n\nClose 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.\n\nEngineer the event lake. Join Segment product events in S3/Iceberg to CRM records to measure product-qualified leads and PLG conversion at scale.\n\nBuild 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.\n\nRun experiments and measure spend. Design A/B and holdout tests, and report CAC, payback and channel ROI with Finance.\n\nUse 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.\n\nKeep it reliable. Own source freshness, data tests, CI checks, cost and monitoring so a broken pipeline never reaches a board deck.\n\nYou have\n5+ years in analytics, with at least 2 focused on marketing, growth or go-to-market analytics at a B2B SaaS company.\n\nExpert 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.\n\nProduction dbt experience: you've built layered models, written tests, and shipped through code review and CI.\n\nFluency in Python for analysis and statistics (pandas, statsmodels or similar), including experiment design and causal inference basics.\n\nDeep 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).\n\nWorking knowledge of attribution approaches and their trade-offs, plus funnel and cohort analysis.\n\nComfort with Git, pull requests and the command line.\n\nA track record of influencing decisions: you can explain a model's assumptions to a CMO in two sentences and defend them under questioning.\n\nNice to have\nExperience with Airflow or another orchestrator, and with ingestion tools such as Fivetran or Estuary.\n\nSemantic layer or metrics-layer experience (dbt Semantic Layer, Omni, Looker).\n\nData lake experience: Iceberg, Spectrum, Athena or Spark.\n\nLead scoring, propensity or marketing-mix modeling.\n\nExposure to sales engagement and revenue intelligence data (Outreach, Gong) or job-change signals.\n\nExperience using LLMs and AI tools to speed up analytics work, with the rigor to check their output.\n\nBy applying for this position, you acknowledge that you have fully read and understand the job requirements and received the Envoy Privacy Notice for applicants, which is linked here. Completing this application requires you to provide personal data, such as your name and contact information, which is mandatory for Envoy to process your application. 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