Data Operations Team Lead

Retailers and CPG brands rely on product data to make critical decisions, but outdated systems limit what’s possible.
Harmonya changes that.

Our AI-powered solutions transform fragmented, inconsistent product data into a dynamic, structured, and enriched source of truth. By analyzing trillions of alternative data points, we help leading CPGs and retailers—including Coca-Cola, Nestle, PepsiCo, and more—gain deeper insights, improve product discovery, and make smarter, faster decisions.

About the position

As Data Operations Team Lead, you will lead the team responsible for turning client needs into accurate, reliable data, from the client's product data and attribute definitions, through AI model runs, to the approved final output. You will set the team's data handling standards, QA methodology and processes, and build a team that delivers to that standard consistently and at scale.

The team is made up of data specialists who prepare client data, run it through our AI models and check the quality of the results. You will equip them with clear methods, tools and metrics, and develop their ability to read data from the client's point of view. You will work closely with US-based clients, Customer Success and Go-to-Market teams to translate business questions into data requirements, while staying close to the data yourself.

Responsibilities

• Lead, coach and grow the Data Ops team. Own priorities, task assignment, performance, hiring, and clear growth paths that build each person’s skills and ownership.
• Own the quality of every client delivery: standards, quality checkpoints before each data run, acceptance thresholds, escalation paths, peer review and sign-off. Track quality metrics per delivery and act on trends.
• Define and maintain the team’s QA methodology and playbook. This includes sampling methods, precision and completeness metrics, test sets, root-cause analysis, and clear, standardized guidelines for defining and reviewing data.
• Design and scale the team’s operational processes for new products and data domains, from input validation and data definitions through quality checks to sign-off and ongoing monitoring. Work with delivery, R&D and Product teams on timelines, SLAs, tooling and data needs.
• Set the standard for reading data from the client’s point of view. Partner with US-based clients, Customer Success and GTM teams to understand use cases, scope requirements, answer data questions and support sales with samples, feasibility checks and data deep-dives.
• Evaluate LLM and ML outputs at scale, and guide prompt design, model choice and cost-versus-quality trade-offs. Bring AI into the team’s own workflows through automated QA checks, LLM-as-judge evaluation and agents for repetitive data tasks.
• Stay hands-on with the data: audit samples and investigate issues using SQL and Python. Set standards for how data is produced, whether through AI models, rule-based logic or manual review, and build a self-sufficient team that runs deliveries end to end with minimal R&D support.

Requirements

• Data expert with a methodical approach. 5+ years in data roles (data analysis, data quality, data operations or similar), with proven experience designing QA methods: sampling, metrics, test sets, acceptance criteria and root-cause analysis.
• Management experience. 4+ years leading a data, analytics or data operations team. Has built a team and processes that scaled, ideally while the product was still being defined. Delegates ownership and develops people.
• Business orientation. Understands how clients use data to make decisions, and reviews data from the client's point of view.
• English communication. Native-level or full professional proficiency, with experience working directly with US clients or US-based teams.
• AI and LLM experience. Hands-on work with LLMs and ML models: prompt design, model comparison, and evaluating output quality (precision, error analysis).
• Hands-on with data. Strong SQL; comfortable debugging data issues and reading pipeline configuration. Curious, digs into the data personally, and does not stay at the dashboard level.

Advantage

• Experience with CPG, retail or product catalog data (UPCs, taxonomies, item masters).
• Background in Python, BigQuery and Airflow.
• Experience building LLM agents or automation for data and QA work.
• Experience in a fast-growing startup.