Note: The job is a remote job and is open to candidates in USA. Dice is hiring a Staff / Lead-level Data Analyst / Analytics Engineer to embed with the Monetization Data Science & Analytics team. The role serves as a senior individual contributor and technical leader, driving analytics initiatives, data modeling, production pipelines, dashboards, metric governance, and self-serve analytics for advertiser revenue and monetization performance.
Responsibilities
- 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
- 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
- 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
Skills
- · **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
- · **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
- · Experience contributing to or reviewing **design docs and RFCs** for data platforms and pipelines
- · 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
- · Strong grasp of **KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology**
- · Comfort reading experiment results and challenging methodology when needed
- · **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
- · **dbt** or equivalent transformation framework experience strongly preferred
- · **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
- · 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
Benefits
- Remote work arrangement
Company Overview
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