Director of Engineering – Decision Intelligence Platform

AT&T

Director leading the engineering efforts for an AI/ML-driven Decision Intelligence platform at AT&T. Focus on strategy, architecture, and delivering scalable solutions for enterprise decision-making.

Posted 4/29/2026full-timeDallas • Texas • 🇺🇸 United StatesLead💰 $210,600 - $316,000 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsETLGoogle Cloud PlatformJavaKafkaKubernetesMicroservicesNode.jsNoSQLSparkSQL

About the role

Key responsibilities & impact

Set vision and roadmap for an AI/ML-driven Decision Intelligence platform enabling NBA/NBO, journey orchestration, and real-time personalization across channels.
Partner with Marketing, Product, Analytics, and Business leaders to prioritize high-impact use cases and deliver measurable outcomes (uplift, conversion, churn, CLV, cost-to-serve).
Define the reference architecture across event streaming, identity, feature store, MLOps, decision services, and activation—optimized for low latency, scale, governance, and explainability.
Evaluate and adopt new capabilities (real-time inference, bandits/experimentation, privacy-enhancing techniques) based on business value and operational readiness.
Build and lead a high-performing org across software, data, ML, and analytics; drive a platform culture of reusable components, productized APIs, self-service tooling, and operational excellence.
Drive execution via clear OKRs, prioritization, dependency management, and cross-functional operating rhythms with MarTech, IT, Governance, Security, and channel teams.
Deliver core platform capabilities: batch/streaming pipelines, real-time signals ingestion, identity/profile, feature pipelines (online/offline parity), model serving and monitoring (drift), decision engine (models + rules + constraints), and orchestration/activation integrations.
Integrate with enterprise systems (CRM/CDP/CMS/commerce/data platforms) using API-first and event-driven patterns.
Implement closed-loop measurement and optimization through experimentation, performance reporting, and feedback pipelines to continuously improve decision policies and models.
Own reliability and compliance: privacy-by-design, data integrity, auditability, observability, CI/CD, SRE practices, and 24x7 SLAs/SLOs (incident response, runbooks, resilience testing).

Requirements

What you’ll need

15+ years in technology, including 8+ years leading cross-functional teams across engineering, data, and ML for customer-facing platforms.
10+ years building cloud-scale data platforms (lake/warehouse, ETL/ELT, streaming).
Proven integration with MarTech/CDP/journey orchestration and activation ecosystems (Adobe/Salesforce/Marketo/HubSpot or similar), focused on real-time activation and personalization.
End-to-end ownership of platform architecture through production operations, including governance and lifecycle management.
Strong record of building high-performing teams (including managers of managers) and aligning business and technical stakeholders.
Product/platform mindset with disciplined prioritization to drive adoption and measurable customer outcomes.
Clear communicator who can explain NBA/NBO, experimentation, and model risk to non-technical audiences.
Ability to manage competing priorities while maintaining quality, security, and reliability.
Expertise in cloud-scale distributed systems and low-latency, high-throughput real-time decisioning (API-first, event-driven architectures; Azure/AWS/GCP).
Hands-on experience with key stack components: Adobe Experience Platform (AEP) (RT-CDP, AJO, Target, CJA) Streaming (Kafka, Flink, Spark or equivalent) Data platforms (Snowflake/lakehouse), strong SQL and NoSQL Microservices (Java/Node), Kubernetes, API gateway/service mesh patterns.
Observability & reliability (logs/metrics/traces, alerting, SLO/SLA management).
Deep understanding of Decision Intelligence patterns: NBA/NBO, orchestration, eligibility/constraints, frequency capping, and omnichannel policy consistency.
Proven production ML delivery: feature engineering, feature store parity, model building, model serving, MLOps, monitoring/drift, retraining, and experimentation (A/B, uplift; bandits where appropriate).
Strong modern engineering practices: Agile/SAFe, DevOps/MLOps, CI/CD, IaC, secure-by-design, and automated testing for data/ML/decision logic.
Experience using AI coding assistants to accelerate delivery while maintaining code quality and ownership.
Ability to build measurement and reporting for decision performance (KPIs, funnels, offer/model performance, closed-loop feedback).

Benefits

Comp & perks

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.

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI/MLdecision intelligencecloud-scale data platformsETLreal-time decisioningfeature engineeringMLOpsAgileDevOpsautomated testing

Soft Skills

clear communicatorteam leadershipprioritizationstakeholder alignmentcross-functional collaborationquality managementoperational excellenceadaptabilitystrategic visionproblem-solving