Mayo Clinic
Lead AWS Data Engineer
Mayo Clinic - Granite Heights, Wisconsin, United States
Work at Mayo Clinic
Overview
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Overview
Lead AWS Data Engineer
Location:
Onsite -Irving, TX Employment Type:
Full-Time, Direct Hire Compensation:
$150, 000-$190, 000 base + bonus
We're hiring a Lead Data Engineer to architect and build scalable, high-performance data systems that power enterprise analytics and business insights. This role is hands-on, working onsite in Irving, TX, and focused on optimizing large-scale pipelines, infrastructure, and data platforms in a hybrid cloud environment.
Key Responsibilities:
Design and maintain robust data pipelines (batch, streaming, CDC) using Spark, Kafka, Flink, and Trino Build cloud-native data solutions using AWS, Azure, or GCP Develop reusable ETL/ELT frameworks and data models supporting structured and unstructured data Implement orchestration tools like Apache Airflow and manage Kubernetes-based data workflows Collaborate with cross-functional teams to ensure secure, scalable, and high-performing systems Mentor junior engineers and drive data quality and governance standards
Requirements: 7+ years in data engineering with expertise in Python, SQL, Spark, Kafka Strong experience with data lakes, data warehouses, and NoSQL databases Cloud experience with AWS, Azure, or GCP Proven success managing petabyte-scale data and HPC workloads Bachelor's degree in CS, Engineering, or related field;Master's preferred
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Onsite -Irving, TX Employment Type:
Full-Time, Direct Hire Compensation:
$150, 000-$190, 000 base + bonus
We're hiring a Lead Data Engineer to architect and build scalable, high-performance data systems that power enterprise analytics and business insights. This role is hands-on, working onsite in Irving, TX, and focused on optimizing large-scale pipelines, infrastructure, and data platforms in a hybrid cloud environment.
Key Responsibilities:
Design and maintain robust data pipelines (batch, streaming, CDC) using Spark, Kafka, Flink, and Trino Build cloud-native data solutions using AWS, Azure, or GCP Develop reusable ETL/ELT frameworks and data models supporting structured and unstructured data Implement orchestration tools like Apache Airflow and manage Kubernetes-based data workflows Collaborate with cross-functional teams to ensure secure, scalable, and high-performing systems Mentor junior engineers and drive data quality and governance standards
Requirements: 7+ years in data engineering with expertise in Python, SQL, Spark, Kafka Strong experience with data lakes, data warehouses, and NoSQL databases Cloud experience with AWS, Azure, or GCP Proven success managing petabyte-scale data and HPC workloads Bachelor's degree in CS, Engineering, or related field;Master's preferred
#J-18808-Ljbffr