Lead Data Engineer - Hybrid (Houston or Dallas, TX)

Company Description

Work with Us. Change the World.

At AECOM, we're delivering a better world. Whether improving your commute, keeping the lights on, providing access to clean water, or transforming skylines, our work helps people and communities thrive. We are the world's trusted infrastructure consulting firm, partnering with clients to solve the world’s most complex challenges and build legacies for future generations.

There has never been a better time to be at AECOM. With accelerating infrastructure investment worldwide, our services are in great demand. We invite you to bring your bold ideas and big dreams and become part of a global team of over 50,000 planners, designers, engineers, scientists, digital innovators, program and construction managers and other professionals delivering projects that create a positive and tangible impact around the world.

We're one global team driven by our common purpose to deliver a better world. Join us.

Job Description

We are seeking a Lead Data Engineer with deep AWS expertise to guide the design, development, and optimization of our enterprise-scale data pipelines and products. In this role, you will not only contribute technically but also provide leadership to a team of data engineers, partner closely with data architects, and play a key role in planning, estimating, and resourcing major data initiatives. You’ll work on high-impact projects that integrate and transform large volumes of data from multiple enterprise systems into reliable, accessible, and high-quality data products that power analytics, reporting, and decision-making across the organization.  

This position will offer flexibility for hybrid work schedules to include both in-office presence and telecommute/virtual work, to be based from either Houston or Dallas, TX.

Key Responsibilities: 

• Lead the end-to-end design, development, and optimization of scalable data pipelines and products on AWS, leveraging services such as S3, Glue, Redshift, Athena, EMR, and Lambda. 

• Provide day-to-day technical leadership and mentorship to a team of data engineers—setting coding standards, reviewing pull requests, and fostering a culture of engineering excellence. 

• Partner with data architects to define target data models, integration patterns, and platform roadmaps that align with AECOM’s enterprise data strategy. 

• Own project planning, estimation, resourcing, and sprint management for major data initiatives, ensuring on-time, on-budget delivery. 

• Implement robust ELT/ETL frameworks, including orchestration (e.g., Airflow or AWS Step Functions), automated testing, and CI/CD pipelines to enable rapid, reliable deployments. 

• Champion data quality, governance, and security; establish monitoring, alerting, and incident-response processes that keep data products highly available and trustworthy. 

• Optimize performance and cost across storage, compute, and network layers; conduct periodic architecture reviews and tuning exercises. 

• Collaborate with analytics, reporting, and business teams to translate requirements into reliable, production-ready data assets that power decision-making at scale. 

• Stay current with the AWS ecosystem and industry best practices, continuously evaluating new services and technologies to enhance AECOM’s data platform. 

• Provide clear, concise communication to stakeholders at all levels, articulating trade-offs, risks, and recommendations in business-friendly language. 

Qualifications

Minimum Requirements: 

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related discipline plus at least 8 years of hands-on data engineering experience, or demonstrated equivalency of experience and/or education

• 3+ years in a technical-lead or team-lead capacity delivering enterprise-grade solutions. 

• Deep expertise in AWS data and analytics services: e.g.; S3, Glue, Redshift, Athena, EMR/Spark, Lambda, IAM, and Lake Formation. 

• Proficiency in Python/PySpark or Scala for data engineering, along with advanced SQL for warehousing and analytics workloads. 

• Demonstrated success designing and operating large-scale ELT/ETL pipelines, data lakes, and dimensional/columnar data warehouses. 

• Experience with workflow orchestration (e.g.; Airflow, Step Functions) and modern DevOps practices—CI/CD, automated testing, and infrastructure-as-code (e.g.; Terraform or CloudFormation). 

• Experience with data lakehouse architecture and frameworks (e.g.; Apache Iceberg).