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Data Engineer-reading, PA-Onsite

KeylentReading, PA🇺🇸United States

Quick overview

SeniorityMid Senior
Work modeHybrid
LocationReading, PA, United States
DockerSQLGitApache SparkAWSELKSnowflakeAirflowApacheBambooConfluenceGrafanaKafkaPrometheusPythonPostgreSQLKubernetesData AnalysisCI/CD

Job description

Sr Data Platform Engineer / Platform Lead

Location: Reading, PA
Employment Type: Full-Time

Position Summary:

  • As a Data Platform Engineer/Platform Lead within Digital Products, you'll help build and run the data platform behind our fleet and telematics products — from ingesting raw data through to the datasets and views that power our customer-facing applications. You'll work closely with Data Science and product teams, balancing hands-on pipeline development with platform-level decisions on cost, reliability, and scale.

  • Responsibilities

  • Design, build, and maintain data pipelines and orchestration workflows that move data from ingestion through transformation to serving layers

  • Write and optimize SQL and PySpark for large-scale data processing and transformation

  • Tune cloud data warehouse performance and cost (query optimization, refresh/scheduling strategy, resource sizing) as data volume and usage scale

  • Build and maintain data quality checks and pipeline monitoring/alerting to catch issues before they reach downstream consumers

  • Partner with Data Science to support feature engineering and analytics pipelines feeding ML/analytics workloads

  • Collaborate with product and engineering stakeholders to translate business needs into reliable, scalable pipeline design

  • Support incident response, root-cause analysis, and continuous improvement of the data platform

  • Document architecture and pipeline design to support onboarding of new team members

  • Contribute to platform-level decisions on architecture, tooling, and technical direction as the team and data volume scale

Core Skill Set Required

  • Strong SQL skills, including query optimization

  • Proficient in Python and PySpark for large-scale/distributed data processing

  • Hands-on experience with a workflow orchestration tool (e.g., Apache Airflow)

  • Experience with a cloud data warehouse (e.g., Snowflake)

  • Experience with relational databases (e.g., PostgreSQL)

  • Experience with cloud data infrastructure (AWS), including cloud storage and distributed processing (e.g., S3, EMR/Spark)

  • 8+ years in data engineering with hands-on ownership of production data pipelines

  • Strong communication skills; able to explain technical tradeoffs to both technical and non-technical stakeholders

  • Good to Have

  • Familiarity with streaming or change-data-capture (CDC) ingestion patterns (e.g., Apache Kafka)

  • Experience building data quality frameworks and pipeline observability/alerting

  • Exposure to ML/analytics pipeline support (feature engineering, model-serving data prep)

  • Familiarity with monitoring and logging tools, such as Dynatrace, Prometheus, Grafana, or the ELK stack

  • Strong knowledge of DevOps principles, continuous integration, continuous delivery, and infrastructure automation

  • Hands-on experience with containerization technologies like Docker and container orchestration platforms like Kubernetes or OpenShift

  • Experience with CI/CD and collaboration tooling such as Bamboo, Bitbucket, and Confluence

  • Relevant certifications (e.g., SnowPro Core, SnowPro Advanced: Data Engineer, AWS Data Analytics)

  • Experience building or supporting SaaS products

  • Specialized experience with secure data sharing in Snowflake

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