Data and AI Engineering Lead

We are hiring a senior, hands-on Technical Lead to own the data products, internal tools, and AI workflows that power our business operations.

In this role, you will stay fully involved throughout the delivery lifecycle—from initial scoping through implementation, production validation, and user adoption. You will build and debug data integrations, guide Partner Dashboard delivery, resolve complex client reporting issues, and turn recurring operational processes into reliable, automated systems. You will serve as the technical bridge between engineering, account management, tracking specialists, and operations.

What You Will Own
Data Pipelines & Reporting Accuracy:
Build and maintain robust API integrations, ingestion pipelines, and data warehouse transformations.

Reconcile affiliate network and analytics data against client reports—managing brand mapping, timezone shifts, currency conversions, reversals, historical backfills, and customer definitions.

Investigate reporting discrepancies, document standardized business logic, and rigorously validate data fixes.

Partner Dashboard & Technical Delivery:
Translate client and internal stakeholder requests into clear requirements, acceptance criteria, and a prioritized backlog.

Implement data and integration updates, coordinate core application changes with engineering teams, and conduct code and data reviews.

Perform pre-release and production checks, manage release communications, and ensure seamless user adoption.

Client Troubleshooting & Measurement:
Join account and client meetings to diagnose issues related to reporting access, dashboard behavior, tracking, and attribution.

Provide pragmatic workarounds, articulate technical platform boundaries, and drive permanent fixes.

Partner with tracking specialists to align UTMs, attribution windows, subscriptions, and new-customer metrics, verifying fixes directly with clients and account owners.

System Reliability & Incident Response:
Monitor data freshness, completion status, and scheduled workflow execution across all systems.

Diagnose failed or stalled jobs alongside engineering, executing safe retries and backfills while checking for duplicates or missing records.

Maintain monitoring alerts, operational runbooks, and incident post-mortems with dedicated engineering owners to minimize recovery times.

Applied AI & Workflow Automation:
Design, deploy, and maintain reusable AI skills, custom connectors, and automated workflows for data onboarding, campaign administration, reporting, and alerts.

Manage vendor setups, access permissions, versioning, and human-in-the-loop review checkpoints.

Test AI outputs against source data, handle edge cases or missing inputs, and track adoption, accuracy, and operational time savings.

Technical Leadership & Team Enablement:
Coordinate engineering and operations contributors, conduct constructive work reviews, and coach colleagues through evolving processes.

Maintain complete documentation, onboarding guides, and cross-team backup coverage.

Balance planned roadmap initiatives with operational support requests, keeping dependencies, tradeoffs, and delivery timelines transparent to leadership.

Skills

- Core Data & Engineering: Strong, hands-on Python and SQL skills with proven experience building and supporting production data pipelines and warehouse models.
- Integrations & Debugging: Hands-on experience debugging REST APIs, authentication protocols, pagination, schema shifts, and cross-system data discrepancies.
- Modern Data Stack: Practical experience with a cloud data warehouse (e.g., Snowflake, BigQuery), a data transformation framework (e.g., dbt), and a workflow orchestrator (e.g., Airflow, Prefect, Dagster), alongside cloud deployment, logging, and monitoring tools.
- Product Delivery & Software Best Practices: Track record of shipping internal tools or data products from initial requirements through testing, release, and user acceptance using Git version control and code reviews.
- Applied AI & Automation: Experience building practical LLM integrations and automations—including output evaluation, access permissions, robust error handling, and human-in-the-loop validation mechanisms.
- Stakeholder Communication: Exceptional ability to communicate clearly with clients, account managers, and non-technical stakeholders to translate business needs into technical solutions.

Affiliate, ecommerce or marketing measurement including attribution, partner commissions, currencies and order adjustments; BigQuery, dbt, dlt, Dagster and Google Cloud Run or closely related tools; integrations with Impact, Awin, CJ, Everflow, Rakuten or GA4; Claude Code, reusable skills, Model Context Protocol connectors and tools such as Make or Retool; and experience supporting a shared platform used by several client accounts with separate data and access requirements.