Lead Analytics Engineer

Innodata(Nasdaq: INOD) is a global data engineering company. We believe that data and Artificial Intelligence (AI) are inextricably linked.

Our mission is to enable the responsible advancement of artificial intelligence by providing the data, evaluation frameworks, and human expertise required to build AI systems that can be trusted  provide a range of transferable solutions, platforms, and services for Generative AI / AI builders and adopters. In every relationship, we honor our 36+ year legacy delivering the highest quality data and outstanding outcomes for our customers.

Scope of 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.

What You’ll Own:

• 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.

• 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…