Senior Analytics Engineer, Sales Analytics (GTM Data Engineering)

Klaviyo•Boston, MA

About The Position

Data is at the heart of every decision made at Klaviyo, and we’re looking for a Business Intelligence Data Engineer to join our Go To Market (GTM) team supporting Sales Analytics. This domain of data aims to improve the experience of Sales Operations and Analytics. Secondary to this, the role will support ancillary functions of the Partnerships Organization and collaborate with the CS&S organization. This role sits in Data Engineering as part of the GTM team, which is part of a hub and spoke model of analytics engineering at Klaviyo. You’ll build and steward the source of truth for Sales Operational data so People leaders and analysts can answer questions quickly and confidently and turn those insights into a more incentivized, higher-performing organization. You will directly support the sales analytics teams at Klaviyo, working cross functionally with Systems, Deal Ops,, Audits, Planning, and Business Intelligence. You will be an independent self-serving, embedded partner to all of GTM leadership where Sales subject matter expertise is required, capable of translating ambiguous requirements into stable data products. You’ll be supported by the broader Data Engineering organization’s standards, tooling, and review practices.

Requirements

3–5+ years in analytics/data engineering with production ELT in Snowflake + dbt + SQL; Python for orchestration/utilities.
Strong demonstration of Sales Operations
Demonstrated independence partnering with senior, non‑technical leaders; able to translate open‑ended needs into scalable data products.
Proven experience implementing tests, monitoring, and documentation that keep pipelines healthy and reporting trustworthy.
Experience building data integrations and reverse‑ETL pipelines that support business operations.

Nice To Haves

Airflow (orchestration) and Fivetran/Workato (ELT/integration).
Familiarity with data privacy controls (masking/RLS) in people data.
AWS experience (S3/EC2/Lambda) and IaC/Terraform.

Responsibilities

Deliver Sales specific data modeling that drives better operational experience, is SOX compliant, and provides detailed insight into the day to day of operation as well as forecasting.
Maintain and stand up curated, documented marts that make it easy for analytics to operate within a structured governance mandate that enables the Sales operations team to work fast and focus on their organizations with the safety of production ready data.
Own the pipelines & models end‑to‑end. Build and maintain reliable integrations from core sales systems (e.g., CRMs/ROP/ERPs), model them in dbt, and publish governed marts and reverse‑ETLs to operational destinations where they create value.
Create attainment views with Compensation and People Analytics. Partner with analysts to build holistic views of the Sales lifecycle. Examples of focus are quicker cadences to booking and dynamic reconciliation processes
Raise the bar on data reliability and governance. Instrument monitoring and alerting, tests (freshness/volume/constraints), and documentation so the sales data ecosystem is discoverable, auditable, and self-serveable.
Operate as a trusted partner to leadership. Work directly with Operations and Sales leadership to scope problems, clarify trade‑offs, and communicate technical concepts in exec‑ready language.
Integrations & ingestion: Own secure ingestion from ROP/ERP/performance systems into Snowflake; define SLAs/SLOs; implement monitoring & alerting for each feed.
Modeling & marts: Design dimensional/entity models (dbt) for employees, positions, org structure, performance history, forecasting, and pipeline movement; publish curated marts with strong contracts and lineage.
Reverse ETL: Operationalize high‑value models to downstream tools and workflows using reverse‑ETL patterns to close the loop between insight and action.
Quality & governance: Implement tests (unit/integration, schema/freshness), multi-layered validation frameworks that routinely validate data integrity, data policies (masking, purpose‑based access), and documentation that enable safe self‑service across the analytics community.
Repository stewardship: Maintain the analytics codebase (dbt repo), perform code reviews, and ensure modular, reusable patterns the broader team can adopt.
Stakeholder partnership: Run an intake & engagement model with Revenue and Sales Operations/ Analytics (primary), HRIS/People Tech (security/integration), Finance (plan/comp interfaces), and BI/Platform teams (shared standards).