Technical Program Manager, Data Engine

About the role

The team delivers data workflows so operators produce high quality, high quantity, and varied data. You guide data operators with task definitions derived from data ontology so requirements are clear and actionable.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

Data ontology receives task definitions that make requirements clear and actionable for data operators. Project management for data annotation or collection keeps timelines intact and output quality high. Documentation of processes and guidelines creates a shared reference for data teams and operators. Data operators run hands on annotation during the design phase to validate new task concepts quickly. Standardized workflows for annotation or collection ensure consistent execution across projects. Clear written and verbal communication guides data operators so tasks and expectations are understood. The team manages unexpected challenges during data projects without losing momentum. Decisions about AI data are informed by an intermediate level understanding of ML context. Competing requests are prioritized so work moves quickly while remaining organized and traceable. Team members maintain a hardcore attitude while treating people with respect and empathy.

Requirements

The posting states a bachelor's degree requirement. Data operators receive guidance so tasks and expectations are understood clearly. The role manages unexpected challenges that appear during data projects without losing momentum. Key stakeholders including data operators, engineering, and support are owned to align priorities and unblock work. An intermediate level understanding of ML context informs decisions about AI data. Competing requests are prioritized so work moves quickly while remaining organized and traceable. A hardcore attitude is maintained while treating people with respect and empathy in all interactions. A degree is required as stated in the listing.

Practical notes

You will follow guidance and standards set by the company for data activities. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Roles in robotics often combine structured workflows with fast moving change. Tools for data annotation and collection shape how machine learning experiments are run. Clear communication and organization help teams align on goals and outcomes.

Questions to ask

Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.