Data Engineer II

# SENIOR DATA ENGINEER II, AI NATIVE ## About the Role Life360 is seeking a **Senior Data Engineer II** to help build and maintain its next-generation data platform, supporting real-time decision-making, experimentation, machine learning, and large-scale analytics. In this role, you’ll design and maintain scalable data processing and storage pipelines for a robust, secure data lakehouse, working with high-volume data across streaming and batch workloads. The ideal candidate is a strong data engineer who takes ownership, thrives in a high-ambiguity environment, and wants to build high-impact data products using modern cloud and data engineering technologies. You’ll collaborate with product, analytics, and data science teams while leveraging AI and LLM tools to accelerate development and analysis. ## Key Responsibilities - Design, build, and maintain scalable, distributed data pipelines and systems from data ingestion through ELT and storage.- Support both streaming and batch data processing for real-time analytics, machine learning, and experimentation.- Automate, test, and harden data workflows to ensure reliability at scale.- Architect logical and physical data models that support evolving business needs.- Build and maintain features for machine learning models.- Collaborate with product, analytics, and data science teams to turn data into business value.- Enhance and maintain data processing and storage pipelines supporting a secure data lakehouse.- Leverage LLMs for code generation, analysis, and related engineering work.- Review AI-generated code and output for quality, engineering standards, and testing requirements.- Take ownership of AI-assisted outputs and ensure they meet engineering expectations. ## Required Qualifications - **5+ years of experience working with high-volume data infrastructure.**- Deep expertise with a modern cloud data warehouse or lakehouse platform, such as **Databricks, Snowflake, BigQuery, or similar**, on a major cloud provider such as **AWS, GCP, or Azure**.- Proficiency in **Python and SQL**, including the ability to write and optimize complex SQL queries.- Hands-on experience with **dbt**.- Experience with large-scale data processing using **Spark and/or Presto/Trino**.- Strong understanding of data modeling, partitioning strategies, storage formats, and analytical workload optimization.- Hands-on experience leveraging **LLMs for code generation, analysis, and related work**, including reviewing AI-generated output for quality, standards, and testing.- Experience with modern data engineering tooling, including:- Orchestration such as **Airflow or Databricks Workflows**- CI/CD such as **GitHub Actions**- Infrastructure-as-code such as **Terraform or DABs**- Bachelor’s degree in **Computer Science, Information Systems, Management Information Systems, Statistics, Mathematics, Data Science, Engineering (Computer, Electrical, or Software), or a related quantitative field**. ## Preferred Qualifications - **Databricks** experience is preferred.- Experience with **Snowflake, BigQuery, or similar** cloud data warehouse/lakehouse platforms is considered.- The source job description includes a “Nice to Have” section but does not provide additional qualifications in the supplied text. ## Skills & Competencies - Senior-level data engineering- High-volume data infrastructure- Distributed data pipelines- Streaming and batch processing- Data ingestion and ELT- Data lakehouse architecture- Cloud data platforms- Databricks- Snowflake- BigQuery- AWS, GCP, or Azure- Python- SQL and query optimization- dbt- Apache Spark- Presto/Trino- Data modeling- Partitioning strategies- Storage formats- Analytical workload optimization- Machine learning data infrastructure- Data workflow automation- Airflow- Databricks Workflows- GitHub Actions- CI/CD- Terraform- DABs- LLM-assisted development- AI-assisted code generation and analysis- Code quality, testing, and engineering standards- Cross-functional collaboration- Problem ownership and problem-solving ## Education & Experience **Education** - Bachelor’s degree in Computer Science, Information Systems, Management Information Systems, Statistics, Mathematics, Data Science, Engineering (Computer, Electrical, or Software), or a related quantitative field. **Experience** - 5+ years working with high-volume data infrastructure.- Experience with modern cloud data warehouse or lakehouse platforms.- Experience with Python, SQL, dbt, and large-scale data processing using Spark and/or Presto/Trino.- Experience with modern data engineering tooling and AI/LLM-assisted development. ## Work Arrangement & Schedule - **Location:** Philadelphia, PA, US (listing location)- **Work Arrangement:** Remote- **Remote Policy:** Life360 is a **Remote First** company. Unless otherwise specified, positions can be performed remotely within the **United States and Canada**, regardless of the specified location above.- **Employment Type:** Not specified in the original job description.- **Expected Weekly Hours:** Not specified.- **Schedule Flexibility:** Not specified.- **Weekend Requirements:** Not specified. ## Compensation & Benefits - **US-based salary range:** $148,000–$216,500.- Final base pay may vary considerably based on geographic location, job-related knowledge, skills, and experience.- Compensation includes a range of **medical, dental, vision, financial, and other benefits**.- Equity is included in the compensation package. ## Compliance / Additional Information Life360 is an **AI Native** company where AI is an integral part of how the company builds and operates. Depending on the role, candidates may be asked to demonstrate proficiency with AI tools, discuss how they leverage AI, or complete interview exercises without AI assistance. Recruiters provide guidance during the interview process. For this team, AI/LLM usage includes code generation, analysis, and related use cases. Engineers are expected to review AI-generated output carefully for quality, standards, and testing and to take ownership of the resulting work. Undisclosed AI use during the interview process that was not previously discussed with or approved by the recruiter may impact candidacy.