Big Data Engineering Lead

Turtle Trax S.A.; United States; À distance; USD 150 000 - 210 000; Plein temps

Résumé du poste

Turtle Trax S.A. is seeking a Big Data Engineering Lead to provide technical leadership for the Data Foundation platform.

You will steer the design and delivery of scalable data pipelines, data products, canonical models, and data services that empower analytics, AI, integration, and operational reporting. The ideal candidate is a hands-on leader with deep expertise in modern data architectures, distributed processing, and engineering excellence.

Qualifications

• 8+ years in data engineering or enterprise data platforms.
• 3+ years in technical leadership of engineering teams or large data initiatives.
• Experience designing and delivering enterprise data platforms, pipelines, and data products.
• Experience in agile software development environments.

Responsabilités

• Lead the Data Foundation platform's engineering delivery and quality.
• Oversee building and maintaining enterprise data pipelines, data products, and canonical models.
• Partner with Service Owner, Architecture, and Platform Engineering to deliver scalable data solutions.
• Mentor a high-performing data engineering team and drive engineering excellence.

Connaissances

Advanced SQL and data modeling

Python and PySpark

ETL/ELT architectures

Master Data Management

RESTful APIs and data services

GraphQL and modern integration

Microservices architectures

Event-driven and real-time processing

Data quality, metadata, lineage

Description du poste

Big Data Engineering Lead

Remote

Advanced english

Role Summary

The Data Engineering Lead is responsible for the technical leadership, engineering delivery, and quality of the Data Foundation platform.

This role leads the team responsible for building and maintaining enterprise data pipelines, data products, canonical data models, and data services that enable analytics, AI, integration, and operational reporting. The Data Engineering Lead partners closely with the Service Owner, Architecture Team, and Platform Engineering Lead to deliver scalable, trusted, and maintainable data solutions aligned with enterprise standards and strategic objectives.

The ideal candidate is a hands‑on technical leader with deep expertise in data engineering, modern data architectures, distributed processing, and engineering excellence. They are accountable for engineering execution, solution quality, operational reliability, and the development of a high‑performing data engineering team.

Required Qualifications

Experience

• 8+ years of experience in data engineering, software engineering, or enterprise data platform development.




• 3+ years of technical leadership experience leading engineering teams or large‑scale data initiatives.




• Proven experience designing and delivering enterprise data platforms, pipelines, and data products.




• Experience operating within agile software development environments.

Technical Skills

• Advanced SQL and data modeling expertise.




• Strong Python and PySpark development experience.




• Deep understanding of ETL and ELT architecture patterns.




• Experience implementing Master Data Management solutions.




• Experience building RESTful APIs and data services.




• Familiarity with GraphQL and modern integration approaches.




• Understanding of microservices‑based architectures.




• Experience implementing event‑driven and real‑time data processing solutions.




• Strong understanding of data quality frameworks, metadata management, and data lineage concepts.

Leadership Competencies

• Strong technical leadership and coaching capabilities.




• Excellent problem‑solving and decision‑making skills.




• Ability to balance delivery commitments with long‑term platform sustainability.




• Strong collaboration and stakeholder engagement skills.




• Excellent written and verbal communication abilities.




• Passion for engineering excellence and continuous improvement.

Success Measures

The Data Engineering Lead will be measured on:

• Successful delivery of Data Foundation engineering commitments.




• Data pipeline reliability, performance, and scalability.




• Data quality improvements across enterprise data assets.




• Adoption of engineering standards and development practices.




• Reduction of technical debt and engineering risk.




• Improvement in operational stability and incident reduction.




• Engineering team engagement, capability growth, and retention.




• Increased reuse of canonical data models, data products, and engineering frameworks.