Data Analytics

We are hiring a Staff / Lead-level Data Analyst / Analytics Engineer to embed with the Monetization Data Science & Analytics team as a senior individual contributor and technical leader. This person will be the go-to analytics expert for advertiser revenue, monetization performance, and growth metrics — trusted by Data Scientists, Analysts, PMs, and Engineering leaders to drive high-impact work end-to-end.

This is a staff-level Individual Contributor role, not a mid-level execution seat. The successful candidate operates as:

·         A trusted thought partner to Data Scientists and Product leaders — someone who improves the quality of the question before answering it.

·         A technical leader who sets standards for data models, pipelines, and dashboards that others follow.

·         A force multiplier who unblocks the team by identifying and fixing root causes across the data stack, not just building what''s asked.

The ideal candidate has grown up on the analytics side of the house (Business Analyst, Product Analyst, BI Analyst) and, over 9+ years, developed deep data engineering, modeling, and visualization expertise. They have led complex, cross-functional analytics initiatives and have a track record of raising the bar wherever they''ve worked.

Who this role is NOT for: anyone who executes tickets, waits for direction, escalates data mismatches without triage, or cannot articulate the business rationale behind their work. We''re paying for judgment, ownership, and craft — not hours.

2. Work Mix

·         50% — Analytics, Business Insights & Technical Leadership Partnering with Data Scientists and Product on the hardest analytics problems; driving metric definitions; reviewing others'' analyses; setting standards for the team''s analytics work.

·         30% — Data Engineering & Pipeline Ownership Architecting and owning production SQL pipelines, data models, and data cubes; designing and operating Airflow DAGs; setting the bar for data quality, reliability, and reconciliation across the domain.

·         20% — Data Visualization, Metric Governance & Enablement Owning executive-visibility dashboards in Tableau / Superset; defining and governing metrics; enabling self-serve analytics for the broader Monetization org.

3. Key Responsibilities

Analytics Leadership & Business Partnership

·         Serve as the senior analytics IC for the Monetization Analytics pod — the person Data Scientists and PMs come to with the hardest, most ambiguous data problems.

·         Improve the quality of the question before answering — reframe vague asks into sharper, more valuable analytical approaches.

·         Lead end-to-end analytics initiatives that span data modeling, pipeline work, and dashboard delivery — with minimal supervision and clear stakeholder communication throughout.

·         Set metric definitions and standards for advertiser revenue, monetization performance, funnel/cohort metrics, and experiment readouts — and drive consistency across dashboards.

·         Independently drive root-cause analysis on data discrepancies across dashboards, warehouses, or pipelines — including cross-team debugging when needed.

·         Review, coach, and raise the bar on the work of other analysts and analytics engineers on the team.

Data Engineering & Pipeline Architecture

·         Architect and own production-grade SQL data pipelines (Presto / Trino / Hive / Spark SQL) — including making the right tradeoffs on incremental vs. full refresh, pre-aggregation, and cost/performance.

·         Design and own data cubes, aggregate tables, and semantic layers used by the whole Monetization Analytics function.

·         Author, own, and operate Airflow DAGs for critical revenue and monetization pipelines — including SLAs, on-call posture, backfills, and incident response.

·         Set and enforce standards for data quality, reconciliation, and observability — row counts, revenue tie-outs, distribution checks, anomaly alerting — across the domain.

·         Optimize existing pipelines aggressively for cost and latency (partitioning, incremental refresh, query tuning on billion+ row tables) — and quantify the wins.

·         Contribute to cross-team technical decisions — table designs, upstream schema changes, migration plans (e.g., Hive → Trino) — via design docs and reviews.

Data Visualization, Metric Governance & Enablement

·         Own the design and quality of executive and cross-functional dashboards in Tableau and/or Superset.

·         Drive metric governance — clear definitions, owners, source-of-truth queries, validation, deprecation.

·         Enable self-serve analytics for Data Scientists, Analysts, and PMs — clear naming, documentation, certified metrics, sensible defaults, and coaching.

4. Required Qualifications

Experience

·         9+ years of combined experience in Data Analytics, Business Intelligence, or Analytics Engineering roles.

·         At least 3+ years at Senior level or above in an analytics-adjacent role at a high-scale tech, ads-tech, marketplace, or fintech company.

·         Prior experience as the most senior analytics IC on an embedded team — or a strong case for why they''re ready to step into that role now.

·         Track record of leading end-to-end analytics initiatives — from ambiguous business question through data model, pipeline, dashboard, and rollout.

·         Prior experience partnering directly with US-based Data Science, Product, and Engineering leaders as a full contributor.

Technical Skills — SQL & Data Engineering (Advanced)

·         Expert-level SQL — deep proficiency with window functions, CTEs, complex joins, query optimization, incremental patterns, skew mitigation, and cost tuning on billion+ row tables.

·         Deep hands-on with at least two of: Presto, Trino, Hive, Spark SQL, Snowflake, BigQuery, Redshift. Can reason about query plans and physical layout, not just syntax.

·         Advanced Airflow — has architected and operated large DAG ecosystems (50+ production DAGs), including cross-DAG dependencies, backfills at scale, and SLA management. Equivalent orchestrators (Dagster, Prefect) also acceptable if depth is comparable.

·         Data architecture & modeling depth — Kimball, star schema, dimensional modeling, OLAP cubes, wide fact tables, slowly-changing dimensions, semantic layer design. Can defend design tradeoffs in a design review.

·         ETL / ELT architecture — incremental loads, backfills, idempotency, data quality frameworks, lineage.

·         Python for data work — pandas, PySpark, scripting, and light tooling development.

·         dbt or equivalent transformation framework experience strongly preferred.

·         Experience contributing to or reviewing design docs and RFCs for data platforms and pipelines.

Technical Skills — Visualization

·         Deep, hands-on production experience building executive-grade dashboards in Tableau and/or Apache Superset (Looker, Power BI, Mode also acceptable).

·         Strong opinions on dashboard design — headline vs. drilldown metrics, layout, filters, performance, self-serve UX.

·         Experience driving metric governance and self-serve BI at an org level.

Analytics & Business Skills

·         Strong grasp of KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology.

·         Deep exposure to digital advertising / monetization metrics — impressions, clicks, CTR, CPM, CPC, CVR, ROAS, revenue attribution, incrementality — is strongly preferred.

·         Prior experience at ads-tech, digital media, or major consumer/marketplace tech companies (Meta, Google, Amazon, Uber, DoorDash, Snap, TikTok, LinkedIn, Airbnb, Instacart, Pinterest peers, etc.) is a strong plus.

·         Comfort reading experiment results and challenging methodology when needed.

Leadership, Communication & Ways of Working

·         Native or near-native English (spoken and written) — this is a hard requirement.

·         Track record of leading initiatives end-to-end with minimal direction — scoping, aligning stakeholders, executing, and communicating results.

·         Comfortable pushing back on unclear or misdirected requirements and proposing better approaches.

·         Prolific writer of design docs, RFCs, requirement docs, and postmortems.

·         Experience mentoring or coaching less-senior analysts and analytics engineers — even if not a formal manager.

·         Executive presence — can present analytics work to Director/VP-level stakeholders and defend recommendations.

·         Operates with the ownership mindset of a permanent employee, even in a contract role.

5. Nice-to-Have

·         Prior experience as a Staff / Principal Analytics Engineer, Staff Data Analyst, or Analytics Tech Lead at a top-tier tech company.

·         Experience with A/B testing platforms and complex experimentation analysis (variance reduction, quasi-experiments, incrementality).

·         Solid statistics foundation (hypothesis testing, confidence intervals, regression basics) — enough to challenge a Data Scientist''s methodology.

·         Experience with data catalogs, lineage tools, or metric layer platforms (Amundsen, DataHub, Atlan, Cube, Transform, etc.).

·         Experience contributing to internal analytics platform / tooling improvements.

·         Tableau certification (Certified Associate or Professional) or equivalent.

·         Prior contract or fractional-lead experience with US-based tech companies.

8. Screen-Out Signals (Please Filter These Out)

Candidates who match on tools but show these behaviours are not a fit for a Staff-level role:

·         Resume claims "senior" but the bullets read as execution ("built pipeline X, wrote SQL Y, delivered table Z") with no scope, impact, or business context.

·         Cannot describe an initiative they led end-to-end that spanned analytics + data engineering + visualization.

·         Escalates data mismatches immediately without first-pass debugging.

·         No evidence of mentoring or raising the bar for others.

·         Only executes exactly what''s written in the ticket; no interpretation or judgment.

·         No prior experience partnering directly with US-based Data Science / Analytics / Product leaders.