Senior Data Engineer, Marketing & GTM

Amplitude•San Francisco, CA

About The Position

Join Amplitude’s engineering team building data products, integrations, and AI-enabled tools used across the company. This role supports Marketing and Go-to-Market, partnering with Marketing Operations, Revenue Operations, Sales, and Analytics to build reliable data pipelines, trusted business models, and reusable semantic and context layers. The work enables teams to understand campaign performance, connect marketing engagement to pipeline and revenue, prioritize accounts, and provide consistent business context for analytics and AI workflows. The role is suited to a hands-on engineer who enjoys learning how the business works and owning complex data projects from discovery through production support. It combines strong data engineering fundamentals, sound judgment, curiosity, and a focus on useful outcomes. The engineer owns delivery from design and incremental development through validation, deployment, documentation, and ongoing support, balancing speed with maintainability, performance, and operating cost. The role also contributes to engineering standards, team knowledge sharing, mentoring, and the technical roadmap.

Requirements

At least 5 years of relevant data engineering experience, including ownership of complex production pipelines and data models.
Strong SQL skills and practical Python experience for integrations, automation, and data processing.
Hands-on experience with Fivetran and dbt, including ingestion configuration, incremental transformations, testing, documentation, and troubleshooting.
Production experience with Snowflake or Databricks, including query optimization, workload management, access controls, and cost-aware design.
Experience building semantic layers with governed metric definitions, explicit data grain, entity relationships, and reusable business logic.
Experience with Salesforce, Marketo, Google Ads, or similar CRM, marketing automation, and advertising platforms.
Experience building context layers that make business data understandable and usable by people and AI tools, including metadata, terminology, business rules, and validated examples.
Strong data modeling fundamentals, including dimensional modeling, historical snapshots, slowly changing dimensions, deduplication, and incremental processing.
Experience supporting Marketing or GTM data and understanding how leads, contacts, accounts, campaigns, activities, and opportunities connect.
Production engineering practices using Git, code review, CI/CD, automated tests, observability, and safe deployment and recovery.
Comfort using AI tools such as Cursor, Claude, and Codex for engineering work, with sound judgment about validating outputs and protecting sensitive data.
Ability to shape project scope, explain tradeoffs, deliver value incrementally, and measure whether a solution improved the business.
Clear communication, curiosity about unfamiliar systems, and a collaborative approach to working with technical and nontechnical partners.

Nice To Haves

Experience with marketing attribution, campaign influence, lifecycle marketing, account scoring, or warehouse-to-business-system activation.
Familiarity with Snowflake semantic views, Cortex Analyst, dbt Semantic Layer, or comparable semantic modeling tools.
Experience exposing governed data through APIs, MCP servers, or AI agents, and evaluating the accuracy of AI-generated answers.
Experience using AI development tools to accelerate engineering work while maintaining code quality and data correctness.

Responsibilities

Build and operate production data pipelines using Fivetran, dbt, and Snowflake, integrating CRM, marketing automation, advertising, product usage, and other business data.
Develop custom API connectors when existing integrations do not meet requirements.
Create trusted Marketing and GTM data products for campaign performance, funnel conversion, attribution, account engagement, intent scoring, and pipeline analysis.
Build and maintain semantic layers defining shared business entities, relationships, dimensions, and metrics for consistent use by dashboards, analysts, and AI tools.
Develop context layers for AI-enabled analytics, including business terminology, metric descriptions, synonyms, approved joins, business rules, and verified queries.
Partner with business stakeholders to understand workflows, clarify requirements, validate assumptions, and translate business problems into practical technical solutions.
Own data correctness and reliability through source reconciliation, automated testing, freshness monitoring, failure alerts, root-cause analysis, and recovery procedures.
Own delivery end to end, from design and incremental development through validation, deployment, documentation, and ongoing support.
Balance delivery speed with maintainability, performance, and operating cost.
Raise engineering standards through code and design reviews, reusable patterns, knowledge sharing, and mentoring.
Contribute improvements to the team’s technical roadmap.
Use AI tools in day-to-day engineering work while validating outputs and protecting sensitive data.

Benefits

Medical, dental, and vision insurance, with 100% employer-paid premiums for employees on select plans.
401(k) retirement plan with an employer match of up to 1% of eligible pay each pay period, up to $2,000 annually.
Flexible time off and paid holidays.
Monthly wellness stipends.
Monthly commuter transit and parking stipends.
Quarterly learning and development stipends.
New-hire home office equipment stipend.
12 weeks of paid parental leave.
Fertility, adoption, and surrogacy support.
Back-up child care support.
No-cost employee access to Modern Health coaching and therapy sessions.
Employee Stock Purchase Program (ESPP).
Charitable giving grant and paid volunteer time off.
Mentorship programs, management training, and wellness initiatives.