Senior Analytics Engineer, People Analytics

Klaviyo•Boston, MA

About The Position

At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If you're a close but not exact match with the description, we hope you'll still consider applying. Want to learn more about life at Klaviyo? Visit careers.klaviyo.com to see how we empower creators to own their own destiny. About the team and role Data is at the heart of every decision made at Klaviyo, and we're looking for a Business Intelligence Data Engineer to join our Business Intelligence (BI) team supporting People Analytics. This domain of data aims to improve the experience of all Klaviyos, from culture and engagement to retention and satisfaction. This role sits in Data Engineering as part of the BI team and is fully dedicated to People Analytics as a sponsored headcount. You'll build and steward the source of truth for people data so People leaders and analysts can answer lifecycle questions quickly and confidently and turn those insights into a healthier, higher-performing organization. You will be an independent, embedded partner to People leadership, 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. How you'll make a difference Deliver people‑data SSoT that drives employee experience. Stand up curated, documented marts that make it easy to monitor org health, retention, hiring, performance, movement, and comp cycles so that leadership can act earlier and more precisely. Own the pipelines & models end‑to-end. Build and maintain reliable integrations from core HR systems (e.g., HRIS/ATS/OKR/comp), model them in dbt, and publish governed marts and reverse‑ETLs to operational destinations where they create value. Create lifecycle views with People Analytics. Partner with analysts to build hire→onboard→develop→promote→compensate→offboard lifecycle views and “employee 360” profiles that reduce swivel‑chair reporting and speed up decision cycles. Raise the bar on data reliability and governance. Instrument monitoring and alerting, tests (freshness/volume/constraints), and documentation so the people data ecosystem is discoverable, auditable, and self-serve. Operate as a trusted partner to leadership. Work directly with People leadership to scope problems, clarify trade‑offs, and communicate technical concepts in exec‑ready language.

Requirements

3-5+ years in analytics/data engineering with production ELT in Snowflake + dbt + SQL; Python for orchestration/utilities.
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.
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.
You've already experimented with AI in work or personal projects, and you're excited to dive in and learn fast.
You're hungry to responsibly explore new AI tools and workflows, finding ways to make your work smarter and more efficient.

Responsibilities

Integrations & ingestion: Own secure ingestion from HRIS/ATS/comp/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, requisitions/offers, performance/promo history, compensation/equity, and 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), 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 People Analytics (primary), HRIS/People Tech (security/integration), Finance (plan/comp interfaces), and BI/Platform teams (shared standards).

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