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Principal Engineer
Confirmed live in the last 24 hours
Culver City, CA, USA
Experience Level
Desired Skills
Apache Spark
Data Analysis
Data Structures & Algorithms
  • 10+ years of experience in data engineering and/ or software development
  • Experience with and understanding of event data collection tools like Snowplow, Segment, Google Tag Manager, Tealium, Segment and mParticle, etc
  • Full lifecycle experience implementing compute and orchestration tools like Databricks, Airflow, Talend etc
  • Experience with streaming OLAP engines like Druid, ClickHouse, etc
  • Experience with AWS services like EMR Spark, Redshift, Kinesis, Lambda, Glue, Athena, etc
  • Working knowledge of BI tools such as Tableau, Looker
  • Experience building multi-touch attribution platforms using integrations with walled gardens, and digital platforms
  • Experience building AI/ML personalization products
  • Experience with real-time data systems
  • Experience with Customer Data Platforms (CDPs) and Data Management Platforms (DMPs)
  • Experience with HIPAA, PCI, or other high-security environments
  • Experience working with large data sets (Terabytes of data/ billions of records)
  • Ability to work effectively and positively with others: You have excellent interpersonal skills. You're a good listener. You place the success of the company ahead of any particular idea
  • Proven ability to work across a matrixed organization
  • Highly articulate and an excellent communicator, with strong influencing skills and the ability to communicate well with peers and senior executives, locally and in different regions
  • BS in Computer Science, Information Systems, or a related field
  • Must have legal right to work in the U.S
  • Design and develop scalable data architecture to support near real time events, machine learning pipelines and analytics platforms using our customer data platform (Segment), purpose build pipelines and DBs to activate our systems of intelligence
  • Work closely with product managers to make application data available for product features for platform analytics and personalizations
  • Uphold, introduce, and refine data quality standards, KPI certification methods, and engineering best practices
  • Challenge existing data structures and compute processes
  • Constantly strive to improve engineering workflows and efficiency, and heavily utilize automation whenever applicable
  • Optimize data pipelines for performance, reliability, and scalability
  • Partner with DevSecOps to optimize cloud infrastructure performance and scalability
  • Roll up your sleeves and be directly involved in solving challenging technical problems
  • Manage business-critical systems, set and facilitate departmental goals and contribute to core product strategy through data and software architecture excellence
  • Deliver projects on time and within budget

501-1,000 employees

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