This role is based remotely but if you live within a 50-mile radius of an office [Atlanta, Austin, Detroit, Warren, Milford or Mountain View], you are expected to report to that location three times a week, at minimum.
The Role
As a Data Engineering Manager within the EDAI organization, you will lead the
development of scalable, reliable, and high-performance data infrastructure that powers advanced analytics, experimentation, and machine learning. Your team will be responsible for building and maintaining the foundational data platforms that enable data scientists, analysts, and marketing stakeholders to generate insights, measure performance, and drive data-informed decisions. You will also be responsible for managing on-call rotations and ensuring operational excellence, system reliability, and timely incident response for the production data pipelines.
This role requires a strong background in data engineering, a deep understanding of modern cloud data architecture, and the ability to collaborate across engineering, data science, and business teams. You will play a key role in shaping the data strategy, ensuring data quality and accessibility, and fostering a culture of engineering excellence and continuous improvement.
What You’ll Do
- Lead and mentor a multidisciplinary team of Data Engineers, Scrum Leads, Architects, and Product Managers to build scalable analytic data foundations.
- Architect, design, and implement data ingestion, transformation, and orchestration workflows using modern data engineering tools and cloud-native technologies.
- Ensure high data quality, reliability, and availability across batch and real-time data processing systems.
- Establish and promote best practices in data engineering, including version control, testing, documentation, and CI/CD for data pipelines.
- Contribute to the evolution of the data architecture and help define long-term strategies for data infrastructure and tooling.
Your Skills & Abilities (Recommended Qualifications)
- Bachelor’s degree (Master’s preferred) in Computer Science, Data Engineering, Information Systems, or a related technical field.
- 3+ years of experience managing data engineering or software engineering teams, with a focus on building and maintaining production-grade data infrastructure.
- 7+ years of hands-on experience in data engineering, including designing, developing, and optimizing large-scale data pipelines and ETL/ELT workflows.
- Proficiency in Python, SQL, and distributed data processing frameworks such as PySpark.
- Experience with modern data platforms and tools such as Airflow, dbt, Snowflake, Databricks, Kafka, or similar.
- Experience with cloud architecture systems such as Azure, AWS, GCP, etc.
- Ability to communicate long-term vision and roadmap milestones through compelling storytelling and data-driven insights to influence leadership and ensure strategic alignment.
- Strong understanding of data modeling, data warehousing, and data governance best practices.
- Demonstrated success in collaborating with cross-functional teams and translating business requirements into scalable data solutions.
- Strong leadership and mentoring skills, with a track record of developing high-performing engineering teams.
