Principal, Data Engineer

American Tower

About The Position

The Team We are seeking a Principal Data Engineer to join American Tower’s Information Technology organization’s Data & Analytics team. The team architects, builds, and optimizes cloud-native data foundations that enable analytics, artificial intelligence (“AI”), and enterprise-scale data products. Day to day you will design and evolve the enterprise data platform, lead complex solution design, and embed governance enforcement through technical controls. As a Principal Data Engineer, you will set engineering standards, mentor engineers, and collaborate extensively with decentralized analytics teams across the Global Operations, Finance, and Sales departments to accelerate value delivery. Responsibilities What You Can Offer Us

Requirements

Bachelor’s degree required, with a concentration in Computer Science, Statistics, Applied Mathematics, or a related quantitative field preferred.
A minimum of 8 years of data engineering or data platform development experience with at least 5 years as a senior or principal-level technical leader required.
Expert-level proficiency in Structured Query Language, Python programming language, and large-scale processing with Apache Spark or PySpark.
Deep experience with major cloud platforms such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform, and with modern data platforms such as Databricks or Snowflake.
Strong understanding of streaming technologies such as Apache Kafka or Amazon Kinesis, orchestration tools such as Apache Airflow, and transformation frameworks such as Data Build Tool.
Mastery of data modeling, semantic modeling, and performance tuning across data warehouse, lakehouse, and streaming architectures.
Ability to implement technical governance (data quality, lineage, metadata, access management, and privacy controls) and establish engineering standards (CI/CD, infrastructure as code, and automated testing).
Ability to lead complex technical design, influence without authority, and mentor engineers to raise the technical bar.
Strong written and oral communication skills, including the ability to present ideas and suggestions clearly and effectively.
Ability to work with different functional groups and levels of employees to effectively and professionally achieve results.
Strong organizational skills; ability to accomplish multiple tasks within the agreed-upon timeframes through effective prioritization of duties and functions in a fast-paced environment.

Nice To Haves

Master’s degree in a related field preferred.

Responsibilities

Architect and evolve the enterprise data platform including ingestion, transformation, storage, orchestration, and serving layers for batch and streaming use cases.
Design and implement end-to-end data pipelines, reusable frameworks, and enterprise data models (dimensional, data vault, domain-oriented) to power analytics, AI, and operational use cases.
Define and operationalize the semantic data layer and embed governance enforcement through technical controls (data quality, validation, lineage, metadata, access).
Establish engineering standards for coding, testing, version control, documentation, and continuous integration/continuous delivery (“CI/CD”); optimize cost, performance, and scalability using FinOps principles.
Lead design reviews, root cause analyses, and incident responses; partner with the Data Science team to productionize models and enable real-time/batch inference.
Collaborate with Data Products, IT, security, and architecture teams to deliver secure, resilient, and compliant data products with strong service level agreements.
Evaluate emerging technologies, guide build-versus-buy decisions, and mentor engineers through code reviews, technical talks, and best practices.
Engage decentralized analytics teams to share standards and reusable components while avoiding duplication of efforts.
Other duties as assigned.

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