Lead Data Platform Engineer

TRAC Recruiting

Lead Data Platform Engineer designing scalable cloud-based data solutions in AWS for health tech partner. Collaborating with teams to enhance data platforms and improve system performance.

Posted 5/29/2026full-timeRemote • 🇺🇸 United StatesSenior💰 $155,000 - $165,000 per yearWebsite

Tech Stack

Tools & technologies

Amazon RedshiftAWSCloudETLKubernetesNoSQLPySparkPythonScalaSparkSQLTypeScript

About the role

Key responsibilities & impact

Design, develop, and support scalable data pipelines and cloud-native data architectures.
Build and maintain production-ready applications and data processing frameworks using Python, PySpark, Scala, and related technologies.
Partner with engineering and business stakeholders to deliver analytics-ready datasets and reporting capabilities.
Contribute to technical architecture decisions, platform improvements, and development best practices.
Develop and optimize ETL/ELT workflows and streaming data solutions.
Create and manage data models, reporting structures, and analytics dashboards using AWS-native tools.
Work with relational and NoSQL databases to support high-volume data processing and storage requirements.
Implement cloud infrastructure solutions leveraging AWS services and container orchestration platforms such as Kubernetes or ECS.
Collaborate with operations teams on backup, disaster recovery, data retention, and security strategies.
Participate in code reviews, technical mentorship, and collaborative problem-solving across the engineering organization.
Evaluate and recommend modern technologies and frameworks that improve scalability, efficiency, and reliability.

Requirements

What you’ll need

3+ years of professional experience in software engineering, data engineering, or cloud platform development.
Strong experience building and maintaining scalable data platforms and distributed processing systems.
Proficiency in Python, PySpark, Scala, and/or Typescript.
Hands-on experience developing Spark-based ETL and reporting frameworks using Spark SQL and Spark Streaming.
Experience with AWS cloud services, including tools such as S3, Redshift, Glue, Athena, EMR, Lambda, Step Functions, EventBridge, and QuickSight.
Knowledge of modern data storage and serialization formats such as Parquet and Iceberg.
Experience with relational databases and NoSQL technologies.
Strong understanding of cloud security concepts, including IAM, KMS, encryption, Secrets Manager, and data access controls.
Ability to independently troubleshoot technical challenges and deliver scalable solutions with minimal oversight.
Experience designing cloud-native applications and services in production environments.
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical discipline, or equivalent practical experience.

Benefits

Comp & perks

🌐 Worldwide ❌ Jobs You've Hidden ⭐️ Saved Jobs ✅ Applied Jobs ✉️ Email Alerts 👤 Account TRAC Recruiting Website LinkedIn All Job Openings 11 - 50 employees Founded 2007 🎯 Recruiter 🤝 B2B Recruitment
B2B TRAC Recruiting is a women-owned recruiting and staffing firm focused on high-quality, direct-hire placements across industries. The company emphasizes a “quality over quantity” model, building long-term partnerships with clients by deeply understanding their organizations and roles rather than relying on keyword matching. TRAC has particular experience supporting search funds, SaaS companies, and startups, and its team members each have 10+ years of staffing experience, specializing within different divisions to help clients scale effectively. Lead Data Platform Engineer Job not on LinkedIn 🔥 2 minutes ago 🇺🇸 United States – Remote 💵 $155k - $165k / year ⏰ Full Time 🟠 Senior 🏗️ Platform Engineer Amazon Redshift AWS Cloud ETL Kubernetes NoSQL PySpark Python Scala Spark SQL TypeScript Apply Now Find Hiring Managers Customize resume + cover letter Report problem ☆ Save ☑️ Mark as applied ❌ Hide 📋 Description
Design, develop, and support scalable data pipelines and cloud-native data architectures.
Build and maintain production-ready applications and data processing frameworks using Python, PySpark, Scala, and related technologies.
Partner with engineering and business stakeholders to deliver analytics-ready datasets and reporting capabilities.
Contribute to technical architecture decisions, platform improvements, and development best practices.
Develop and optimize ETL/ELT workflows and streaming data solutions.
Create and manage data models, reporting structures, and analytics dashboards using AWS-native tools.
Work with relational and NoSQL databases to support high-volume data processing and storage requirements.
Implement cloud infrastructure solutions leveraging AWS services and container orchestration platforms such as Kubernetes or ECS.
Collaborate with operations teams on backup, disaster recovery, data retention, and security strategies.
Participate in code reviews, technical mentorship, and collaborative problem-solving across the engineering organization.
Evaluate and recommend modern technologies and frameworks that improve scalability, efficiency, and reliability. 🎯 Requirements
3+ years of professional experience in software engineering, data engineering, or cloud platform development.
Strong experience building and maintaining scalable data platforms and distributed processing systems.
Proficiency in Python, PySpark, Scala, and/or Typescript.
Hands-on experience developing Spark-based ETL and reporting frameworks using Spark SQL and Spark Streaming.
Experience with AWS cloud services, including tools such as S3, Redshift, Glue, Athena, EMR, Lambda, Step Functions, EventBridge, and QuickSight.
Knowledge of modern data storage and serialization formats such as Parquet and Iceberg.
Experience with relational databases and NoSQL technologies.
Strong understanding of cloud security concepts, including IAM, KMS, encryption, Secrets Manager, and data access controls.
Ability to independently troubleshoot technical challenges and deliver scalable solutions with minimal oversight.
Experience designing cloud-native applications and services in production environments.
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Hard Skills & Tools

PythonPySparkScalaTypescriptETLELTSpark SQLSpark Streamingdata modelingcloud-native applications

Soft Skills

collaborative problem-solvingtechnical mentorshipindependent troubleshootingcommunicationstakeholder partnershiporganizational skillsadaptabilitycritical thinkingattention to detailleadership

Certifications

Bachelor's degree in Computer ScienceBachelor's degree in EngineeringBachelor's degree in Mathematicsequivalent practical experience