Staff Data Engineer

Posted 3 months agoViewed

💎 Seniority level: Staff, 7+ years

📍 Location: United States

🔍 Industry: Software Development

🏢 Company: Life360👥 251-500💰 $33,038,258 Post-IPO Equity over 2 years ago🫂 Last layoff over 2 years agoAndroid Family Apps Mobile Apps Mobile

🗣️ Languages: English

⏳ Experience: 7+ years

🪄 Skills: AWSProject ManagementPythonSQLApache AirflowETLKafkaAlgorithmsData engineeringData StructuresREST APISparkCommunication SkillsAnalytical SkillsCollaborationCI/CDProblem SolvingAgile methodologiesMentoringTerraformData visualizationTechnical supportData modelingData analyticsData managementDebugging

Requirements:

Minimum 7 years of experience working with high volume data infrastructure.
Experience with Databricks and AWS.
Experience with dbt.
Experience with job orchestration tooling like Airflow.
Proficient programming in Python.
Proficient with SQL and the ability to optimize complex queries.
Proficient with large-scale data processing using Spark and/or Presto/Trino.
Proficient in data modeling and database design.
Experience with streaming data with a tool like Kinesis or Kafka.
Experience working with high volume event based data architecture like Amplitude and Braze.
Experience in modern development lifecycle including Agile methodology, CI/CD, automated deployments using Terraform, GitHub Actions, etc.
Knowledge and proficiency in the latest open source and data frameworks, modern data platform tech stacks and tools.
Always learning and staying up to speed with the fast moving data world.
You have good communication and collaboration skills and can work independently.
BS in Computer Science, Software Engineering, Mathematics, or equivalent experience.

Responsibilities:

Design, implement, and manage scalable data processing platforms used for real-time analytics and exploratory data analysis.
Manage our financial data from ingestion through ETL to storage and batch processing.
Automate, test and harden all data workflows.
Architect logical and physical data models to ensure the needs of the business are met.
Collaborate across the data teams, engineering, data science, and analytics, to understand their needs, while applying engineering best practices.
Architect and develop systems and algorithms for distributed real-time analytics and data processing.
Implement strategies for acquiring data to develop new insights.
Mentor junior engineers, imparting best practices and institutionalizing efficient processes to foster growth and innovation within the team.
Champion data engineering best practices and institutionalizing efficient processes to foster growth and innovation within the team.

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