Lead Analytics Engineer

Innodata Inc.

5d•CA$130,000 - CA$150,000

About The Position

Innodata is seeking a Staff / Lead-level Data Analyst / Analytics Engineer to join the Monetization Data Science & Analytics team. This role is a senior individual contributor and technical leader, serving as the primary analytics expert for advertiser revenue, monetization performance, and growth metrics. The successful candidate will be a trusted partner to Data Scientists, Analysts, PMs, and Engineering leaders, driving high-impact work from inception to completion. This is not a mid-level execution role, but rather a leadership position focused on thought partnership, setting technical standards, and acting as a force multiplier for the team by addressing root causes within the data stack.

Requirements

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 readiness for such a role.
Track record of leading end-to-end analytics initiatives from ambiguous business questions 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 proficiency, including window functions, CTEs, complex joins, query optimization, incremental patterns, skew mitigation, and cost tuning on billion+ row tables.
Deep hands-on experience with at least two of: Presto, Trino, Hive, Spark SQL, Snowflake, BigQuery, Redshift.
Advanced Airflow experience, including architecting and operating large DAG ecosystems with cross-DAG dependencies, backfills, and SLA management.
Strong data architecture and modeling depth (Kimball, star schema, dimensional modeling, OLAP cubes, wide fact tables, slowly-changing dimensions, semantic layer design).
Experience with ETL/ELT architecture, including incremental loads, backfills, idempotency, data quality frameworks, and lineage.
Proficiency in Python for data work (pandas, PySpark, scripting, tooling development).
Deep, hands-on production experience building executive-grade dashboards in Tableau and/or Apache Superset.
Strong grasp of KPI definition, metric design, funnel analysis, cohort analysis, and A/B testing methodology.
Native or near-native English (spoken and written).

Nice To Haves

dbt or equivalent transformation framework experience.
Experience contributing to or reviewing design docs and RFCs for data platforms and pipelines.
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.
Deep exposure to digital advertising / monetization metrics (impressions, clicks, CTR, CPM, CPC, CVR, ROAS, revenue attribution, incrementality).
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.).
Comfort reading experiment results and challenging methodology.
Experience mentoring or coaching less-senior analysts and analytics engineers.
Executive presence — ability to 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.

Responsibilities

Partnering with Data Scientists and Product on complex analytics problems, driving metric definitions, reviewing analyses, and setting standards for the team's analytics work.
Architecting and owning production SQL pipelines, data models, and data cubes, designing and operating Airflow DAGs, and establishing data quality, reliability, and reconciliation standards.
Owning executive-visibility dashboards in Tableau / Superset, defining and governing metrics, and enabling self-serve analytics for the broader Monetization organization.
Serving as the senior analytics IC for the Monetization Analytics pod, tackling ambiguous data problems and improving the quality of analytical questions.
Leading end-to-end analytics initiatives, including data modeling, pipeline work, and dashboard delivery, with minimal supervision and clear stakeholder communication.
Setting metric definitions and standards for advertiser revenue, monetization performance, funnel/cohort metrics, and experiment readouts, ensuring consistency across dashboards.
Independently driving root-cause analysis on data discrepancies across dashboards, warehouses, or pipelines, including cross-team debugging.
Reviewing, coaching, and elevating the work of other analysts and analytics engineers on the team.
Architecting and owning production-grade SQL data pipelines (Presto / Trino / Hive / Spark SQL), making tradeoffs on refresh strategies, pre-aggregation, and cost/performance.
Designing and owning data cubes, aggregate tables, and semantic layers for the Monetization Analytics function.
Authoring, owning, and operating Airflow DAGs for critical revenue and monetization pipelines, including SLAs, on-call, backfills, and incident response.
Setting and enforcing standards for data quality, reconciliation, and observability across the domain.
Aggressively optimizing existing pipelines for cost and latency and quantifying the impact.
Contributing to cross-team technical decisions through design docs and reviews.
Owning the design and quality of executive and cross-functional dashboards in Tableau and/or Superset.
Driving metric governance, including clear definitions, owners, source-of-truth queries, validation, and deprecation.
Enabling self-serve analytics through clear naming, documentation, certified metrics, sensible defaults, and coaching.