Data Science & Engineering Manager
New
J
JobgetherSecurity & IT
Fully remote work within the United StatesFull-TimeManager
Salary$170,000–$215,000 USD
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Job Details
- Experience
- 5+ years of professional experience in data engineering, with 3+ years managing data engineering, machine learning, or data science teams.
- Required Skills
- PythonSQLApache AirflowGCPSnowflakeData engineeringBigQuerydbtMLOps
Requirements
- 5+ years of professional experience in data engineering.
- 3+ years managing data engineering, machine learning, or data science teams.
- Demonstrated experience coaching senior individual contributors and building inclusive teams.
- Strong hands-on experience managing GCP-based data infrastructure, governance, storage, and optimization.
- Extensive experience with high-volume datasets in BigQuery or Snowflake.
- Proficiency with ELT technologies such as Dataform or dbt.
- Strong knowledge of orchestration technologies such as Apache Airflow.
- Experience designing complex SQL and Python data pipelines.
- Proven expertise with parallelized data processing frameworks such as Dataflow or Spark.
- Familiarity with DevOps practices including Docker, Kubernetes, CI/CD, and observability tools.
- Experience guiding teams through MLOps and end-to-end machine learning lifecycles.
- Ability to communicate complex technical concepts to technical and non-technical stakeholders.
Responsibilities
- Lead, hire, coach, and develop a team of data engineers and applied data scientists.
- Partner with cross-functional leaders to translate business objectives into technical requirements and prioritize initiatives.
- Drive the architecture and delivery of scalable batch and streaming data pipelines and oversee the productionization of machine learning and algorithmic systems.
- Champion data quality, reliability, privacy, compliance, governance, access controls, and cost efficiency across the data platform.
- Develop data cataloging, documentation, and self-service capabilities that make high-quality data more accessible across the organization.
- Guide teams through the full machine learning lifecycle, including MLOps and applied use cases such as recommendation, ranking, personalization, and classification.
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