Engineering Manager, Data Engineering
New
O
OpenlyInsurance Technology
Remote, United StatesFull-TimeManager
Salary176,000 - 264,000 USD per year
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Job Details
- Experience
- 5+ years of data engineering and data platform development experience, including 3+ years of engineering management or technical leadership
- Required Skills
- PythonSQLApache AirflowGCPKubernetesApache KafkaData engineeringTerraform
Requirements
- 5+ years of data engineering and data platform development experience
- 3+ years of engineering management or technical leadership in a data/infrastructure/operations environment
- Deep experience with data infrastructure components (data lakes, warehouses, batch and streaming systems)
- Hands-on expertise designing scalable, secure, and cost-efficient data platforms and pipeline architectures
- Experience with modern data and infrastructure technologies (e.g., Spark, Flink, Kafka, Airflow, Kubernetes)
- Experience with Google Cloud data technologies (BigQuery, Cloud Composer, GCS)
- Infrastructure as Code (IaC) experience with Terraform
- Proficiency in Python or similar languages, with strong SQL skills
- Understanding of data governance, security, and compliance best practices
- Ability to prioritize and work through ambiguity
Responsibilities
- Manage, support, and mentor a diverse group of data engineers
- Partner with the Principal Engineers and engineering leadership to guide the data architecture, scalability, and evolution of our data platform
- Work closely with data science, business intelligence, and product teams to align data solutions with business objectives
- Identify, resolve, or escalate roadblocks and risks that threaten team deliverables and goals
- Drive each team member toward their career goals through clear milestones, transparent feedback, and a culture of open communication
- Translate data concepts and requirements into architecture decisions and technical implementation strategies
- Provide technical input on data pipeline design, data modeling, and technology selections
- Contribute hands-on technical work during peak periods to support the team and maintain technical credibility
- Establish best practices for data pipeline architecture, data quality, and performance optimization
- Run cross functional working groups to deliver on larger company objectives
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