Data platform Manager

Intone Networks

  • Reading, PA
  • 1 day ago

    Highlights

    The role is part of a dedicated cross-functional team and offers the opportunity to establish data engineering discipline, provide technical direction, and help shape the engineering organization as it grows. Design, build, and manage production ETL/ELT data pipelines, including data integration, monitoring, and data quality assurance.

    Numbers & Facts

    LocationReading, PA

    Description

    This is a contract position based in Reading, Pennsylvania, supporting a new strategic initiative focused on greenfield development of a premium SaaS fleet management platform. The role is part of a dedicated cross-functional team and offers the opportunity to establish data engineering discipline, provide technical direction, and help shape the engineering organization as it grows.

    Responsibilities

    • Design, build, and manage production ETL/ELT data pipelines, including data integration, monitoring, and data quality assurance
    • Lead and mentor data engineers, driving technical decisions and communicating effectively with business and technical stakeholders
    • Establish data engineering best practices and discipline within a developing engineering organization
    • Take initiative in providing technical direction and working independently on strategic data platform initiatives
    • Support and lead additional team members as the organization scales
    • Design and implement scalable data architecture, data warehouse solutions, and data modeling approaches
    • Implement workflow orchestration, CI/CD practices, and automated data pipeline deployment

    Qualifications

    • Required
    • 5+ years of hands-on experience designing, building, and managing production ETL/ELT data pipelines
    • Experience leading, mentoring, or managing data engineers
    • Hands-on experience with Snowflake and AWS data infrastructure (S3, EMR, distributed data processing)
    • Strong SQL skills, including query optimization
    • Proficiency in Python and PySpark for large-scale data processing
    • Experience with data warehouse design, data modeling, and distributed processing
    • Experience with Apache Airflow or similar orchestration tools
    • Knowledge of CI/CD, DevOps, and automated data pipeline deployment
    • Preferred
    • Kafka, streaming data, and Change Data Capture (CDC) experience
    • PostgreSQL or other relational database expertise
    • Data observability and monitoring tools (Dynatrace, Prometheus, Grafana, ELK)
    • ML/AI data pipeline support and feature engineering
    • Docker, Kubernetes, or OpenShift experience
    • SnowPro or AWS certifications

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