AECOM-posted 4 months ago
Full-time • Senior
Houston, TX
5,001-10,000 employees

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.

  • 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.
  • BA/BS 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).
  • Strong communication, stakeholder-management, and documentation skills; aptitude for translating business needs into technical roadmaps.
  • Master's degree in a relevant field.
  • Solid understanding of data modeling, data governance, security best practices (encryption, key management), and compliance requirements.
  • Experience working within similarly large, complex organizations.
  • Experience building integrations for enterprise back-office applications.
  • AWS Certified Data Analytics – Specialty or AWS Solutions Architect certification (or equivalent) preferred; experience with other cloud platforms is a plus.
  • Proficiency in modern data storage formats and table management systems, with a strong understanding of Apache Iceberg for managing large-scale datasets and Parquet for efficient, columnar data storage.
  • In-depth knowledge of data cataloging, metadata management, and lineage tools (AWS Glue Data Catalog, Apache Atlas, Amundsen) to bolster data discovery and governance.
  • Knowledge of how machine learning models are developed, trained, and deployed, as well as the ability to design data pipelines that support these processes.
  • Medical, dental, vision, life, AD&D, disability benefits.
  • Paid time off, leaves of absences, voluntary benefits.
  • Flexible work options, well-being resources.
  • Employee assistance program, business travel insurance.
  • Service recognition awards, retirement savings plan.
  • Employee stock purchase plan.
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