Senior Manager, Data Platform Engineering
New
J
JobgetherData Engineering
Based in the United StatesFull-TimeManager
Salary$175,000–$195,000
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Job Details
- Experience
- 8+ years of experience in data engineering, platform engineering, software engineering, or a related technical discipline, including at least 3 years of direct people management
- Required Skills
- AWSApache AirflowPeople ManagementData engineeringSparkCI/CDData modelingPySpark
Requirements
- 8+ years of experience in data engineering, platform engineering, software engineering, or a related technical discipline.
- At least 3 years of direct people management experience within a production engineering environment.
- Proven experience leading complex, cross-functional engineering initiatives from planning through production.
- Strong technical depth across modern data platforms, including data ingestion, ETL/ELT, distributed processing, and cloud-based data architecture.
- Hands-on experience with modern cloud and data technologies such as AWS, S3, Iceberg, Glue, Airflow, Spark/PySpark, or Athena.
- Deep understanding of engineering quality and operational practices, including testing, observability, incident management, and CI/CD.
- Excellent written and verbal communication skills for both technical and non-technical stakeholders.
- Experience building or operating enterprise-scale data lakehouse or infrastructure is highly valued.
- Experience working with healthcare data such as claims or clinical datasets is preferred.
- Familiarity with data governance, metadata management, and regulated data environments is advantageous.
Responsibilities
- Lead, coach, and develop a high-performing Data Platform engineering team through clear expectations, career development, and performance management.
- Own execution of complex data initiatives, translating strategic priorities into realistic delivery plans with defined scope, milestones, and risk mitigation.
- Establish disciplined delivery practices by ensuring initiatives are technically understood, appropriately estimated, and sequenced.
- Build trusted relationships across Product, Client Delivery, Engineering, and Architecture teams to align priorities and resolve technical tradeoffs.
- Drive operational excellence across production support, on-call readiness, incident management, data quality, and observability.
- Guide the evolution of scalable data ingestion, ETL/ELT, distributed processing, and lakehouse cloud architecture.
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