Staff Data Scientist

• Staff Data Analyst · Lead Data Analyst · Principal Data Analyst · Staff Analytics Engineer · Lead Analytics Engineer · Principal Analytics Engineer · Staff BI Engineer ·

1. About the Role

• 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 Qualification
• 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 behaviors 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.