Technical Product Manager, AI & Data Products

AT&T•Dallas, TX

1d•$158,200 - $237,400•Onsite

About The Position

At AT&T, we are building AI-native internal products that help local market teams understand business performance, detect what has changed, and get the right answers faster. This role will build and scale the technical product layer powering AT&T's AI-driven local market intelligence platform. You'll work across the semantic data layer, application services, LLM integration, and front-end experience to deliver tools that help local market leaders identify opportunities, act on recommendations, and accelerate decisions at the DMA level. This role is for a technical product manager who can operate close to the architecture, close to the engineers, and close to the product decisions. You should understand how data is modeled, how services connect, how AI systems behave in production, and how to turn ambiguous business goals into requirements that engineering teams can build. You will work directly with engineers and contractors to scope requirements, write PRDs, shape technical specs, and drive execution. You will partner with the product owner on strategy, business priorities, and stakeholder input. This role is responsible for translating product direction into something precise, scoped, and buildable.

Requirements

6+ years of experience across product management, software engineering, data engineering, or related technical roles.
2+ years of product management experience with direct ownership of PRDs, roadmaps, user stories, and feature prioritization.
Bachelor's degree in CS or a related technical field, or equivalent hands-on experience in full-stack software engineering or software development.
Strong understanding of backend systems, APIs, service architecture, data modeling, and modern application design.
Experience working with structured data platforms, semantic layers, data pipelines, and cloud-based data systems.
Working knowledge of SQL and strong technical fluency in how data moves from source systems into user-facing product experiences.
Experience building or supporting products that use AI or LLM capabilities in production.
Ability to write requirements that engineers trust and can execute without guesswork.
Willingness to move fast, iterate aggressively, fail, rebuild, and improve the product through repeated cycles of learning.
Strong judgment in balancing technical constraints, business needs, and user experience.
Strong organizational and communication skills, able to keep multiple workstreams moving, align cross-functional teams, and communicate clearly across technical and non-technical audiences.
Prior experience as a conduit between external partners and internal product teams, comfortable synthesizing field feedback into actionable product input.

Nice To Haves

Experience building platform products, internal tools, data products, or AI-enabled enterprise applications.
Experience with products that combine metric layers, semantic data layers, dashboards, and natural language interfaces.
Background in software engineering or data engineering before moving into product management.
Experience working with modern data platforms such as Snowflake, Databricks, or similar environments.
Experience in environments that value rapid iteration, strong product instincts, and close collaboration between product and engineering.
Familiarity with telecom, subscription businesses, or other KPI-heavy operating environments is a plus.
Experience working with or alongside AI governance, compliance, or responsible AI teams.

Responsibilities

Translate strategy into buildable product: Translate product strategy and business priorities into scoped, buildable releases, including PRDs, user stories, acceptance criteria, and technical requirements that engineering teams can execute without guesswork.
Work directly with the product owner to turn platform direction into a sequenced roadmap with measurable outcomes.
Develop lightweight prototypes to help guide engineering teams in building solutions that meet user needs.
Turn business needs into clear, scoped releases with defined success metrics tied to DMA-level market outcomes.
Lead discovery and delivery: Run product discovery through research, prototyping, and pilots. Run delivery from MVP through scale. Manage scope, tradeoffs, and dependencies across engineering, data, and design.
Build and maintain a use-case catalog with prioritization based on value, feasibility, risk, and readiness, including playbook use cases and DMA opportunity analysis.
Drive execution: Prioritize features based on user value, technical dependencies, and cost. Partner with engineers and contractors to plan, ship, and improve the product.
Maintain backlog quality, run sprint planning, track progress, and align stakeholders through clear decision points.
Support the operating cadence for the AI platform team, including sprint planning, progress tracking, stakeholder alignment, and input to executive reviews.
Define and enforce human-in-the-loop checkpoints at critical stages of AI-driven workflows to ensure output quality, risk mitigation, and user trust.
Embed AI into team workflows: Design how AI-augmented reporting, insights, and recommendations are embedded into existing team workflows.
Define the interaction model for dashboards, KPIs, and natural language interfaces, prioritizing adoption and speed-to-insight for strategy and GTM teams.
Define agent and LLM capabilities, including reasoning, tool use, human-in-the-loop workflows, and exception handling.
Ensure LLM experiences are grounded in trusted, governed business data and tied to real use cases, including the AI recommendation engine.
Contribute thought leadership on responsible AI product design and work alongside the governance team to ensure products meet compliance, ethics, and data stewardship standards.
Own data and integration requirements: Define and evolve the semantic data layer that governs how business metrics, DMA-level features, and market signals are modeled, accessed, and surfaced across AI products and natural language interfaces.
Drive requirements for data connectors, access patterns, security, privacy, and logging/auditability.
Define how the product integrates with enterprise systems, identity, and role-based access controls.
Ensure data pipelines feeding the product are reliable, governed, and observable.
Connect market leaders to the product: Serve as the connective tissue between sales, local market strategy teams, and internal engineering.
Synthesize field feedback, market leader input, and operational signals into prioritized backlog items that keep the product grounded in how people actually work.
Translate partner and user signals into prioritized backlog items, ensuring the product reflects real workflows across GTM, strategy, and profitability teams.

Benefits

Medical/Dental/Vision coverage
401(k) plan
Tuition reimbursement program
Paid Time Off and Holidays (based on date of hire, at least 23 days of vacation each year and 9 company-designated holidays)
Paid Parental Leave
Paid Caregiver Leave
Additional sick leave beyond what state and local law require may be available but is unprotected
Adoption Reimbursement
Disability Benefits (short term and long term)
Life and Accidental Death Insurance
Supplemental benefit programs: critical illness/accident hospital indemnity/group legal
Employee Assistance Programs (EAP)
Extensive employee wellness programs
Employee discounts up to 50% off on eligible AT&T mobility plans and accessories, AT&T internet (and fiber where available) and AT&T phone