Senior Data Engineer, Ads Data (AI Native)

About Life360

Life360’s mission is to keep people close to the ones they love. Our category-leading mobile app, Tile tracking devices, and Pet GPS tracker empower members to protect the people, pets, and things they care about most with a range of services, including location sharing, safe driver reports, and crash detection with emergency dispatch. Life360 serves approximately 102.4 million monthly active users (MAU), as of June 30, 2026, across more than 180 countries.

Life360 delivers peace of mind and enhances everyday family life with seamless coordination for all the moments that matter, big and small. By continuing to innovate and deliver for our customers, we have become a household name and the must-have mobile-based membership for families (and those friends who are basically family).

Life360 has more than 500 (and growing!) remote-first employees. For more information, please visit life360.com .

Life360 is a Remote First company, which means a remote work environment will be the primary experience for all employees. All positions, unless otherwise specified, can be performed remotely (within the US and Canada) regardless of any specified location above.

We are AI Native

We are building an AI native company where AI is an integral part of how we build and operate. AI tool usage during interviews varies by role. You may be asked to demonstrate proficiency with AI tools, discuss how you leverage AI, or complete interview exercises without AI assistance. Your Recruiter will provide clear guidance as you move through the interview process.

Undisclosed use of AI not previously discussed with or approved by your Recruiter may impact your candidacy.

About The Team

The Ads Data Engineering team's purpose is to design, build, and maintain scalable and efficient data infrastructure that empowers Life360 teams to make data-driven decisions. We transform raw data into reliable, accessible, and actionable insights, ensuring data quality, compliance, security, costs and performance at every step. We help teams across the business unlock the full potential of their data, driving operational excellence and strategic growth. We also push the boundaries of how work gets done by adopting AI tools that accelerate our development and expand what we can deliver to stakeholders, so families get faster, more reliable experiences.

About the Job

At Life360, we collect an enormous amount of data: billions of location points, user actions, and miles driven every month, and so much more. Our analysts, ad strategists, and sales team rely on that data to run campaigns, report performance, and close deals. The core datasets and pipelines behind that work are built and in production, and the demand for new datasets, metrics, and self-serve access is growing. As a Senior Ads Data Engineer, you will own that platform on Databricks: making the existing pipelines faster, cheaper, and more reliable, and extending them as new questions come in. You will work directly with the people who use the data, and you will keep one agreed definition for each core metric so every team sees the same numbers.

For candidates based in the US, the salary range for this position is $129,000 to $192,000 USD. For candidates based out of Canada, the salary range for this position is $149,500 to $177,500 CAD. Note: Please be aware that the job title for positions in Canada will be "Developer" in lieu of "Engineer." We take into consideration an individual's background, job-related knowledge, skills, and experience in determining final salary. Base pay may also vary based on geographic location, with US work locations falling into one of three geographic tiers.

Total compensation is inclusive of equity compensation in the form of Restricted Stock Units (RSUs) , as well as a comprehensive benefits package, including medical, dental, vision, financial, and other benefits.

What You’ll Do

Primary responsibilities include, but are not limited to:

Enhance, extend, and operate our existing batch and streaming pipelines and Delta Lake tables on Databricks, evolving the platform across ingestion, orchestration, storage, and serving as data volume and business needs grow.

Partner with business analysts, ad strategists, and the sales team to understand their questions and turn them into durable data and insights products, such as campaign performance, audience, and revenue reporting.

Build and maintain dbt models and tests for curated Silver/Gold datasets, and own the semantic layer and core business metrics so every team works from a single, trusted definition of performance.

Manage Databricks and AWS infrastructure as code with Terraform.

Integrate and reconcile data from ad platforms, CRM, partner feeds, and internal systems, including migrations off legacy tools and reports.

Implement data quality tests, monitoring, lineage, SLAs, and alerting, and define data contracts with upstream producers, leading root-cause analysis when data breaks.

Tune Spark and Databricks workloads for performance and cost (query plans, partitioning, shuffle and skew, cluster sizing), and make cost-aware design decisions as the platform grows.

Establish engineering best practices (version control, CI/CD, code review, testing, documentation), and mentor engineers and help shape the team’s technical roadmap.

Apply data governance, privacy, and access controls (PII, GDPR, CCPA, SOX) across the platform.

Enhance our LLM development support capabilities by creating tools, skills, and agents that give our LLMs more context and help us debug, build, and maintain systems faster.

Success in Year One

Within the first year, the person hired will be the go-to engineer for our foundational data models and pipelines. Pipeline runtime and compute spend will be measurably lower, and the datasets and metrics that analysts, ad strategists, and sales use will have clear owners and documented definitions. Stakeholders will answer more of their own questions without waiting on the data team, and the first set of enhancements they asked for will be in production.

What We’re Looking For

5+ years of experience in data engineering building production data platforms at enterprise scale, with demonstrated ownership of data products and cross-functional collaboration.

Deep, hands-on expertise with Apache Spark (PySpark and/or Scala), including partitioning and shuffle behavior, joins, skew handling, caching, and diagnosing and tuning slow or costly jobs.

Deep knowledge of the Databricks platform, including Delta Lake, Unity Catalog, Jobs/Workflows, Databricks Asset Bundles, Structured Streaming, and SQL Warehouses, with proven experience optimizing workloads for performance and cost (cluster and warehouse sizing, autoscaling, Photon, serverless).

Strong SQL and Python (or Scala) skills for pipeline development, automation, and query optimization.

Hands-on production experience with dbt, including model design, testing, documentation, and incremental strategies, running on Databricks in CI/CD-driven workflows.

Hands-on experience with Terraform for managing cloud and data platform infrastructure as code, and with AWS (S3, IAM, and networking fundamentals as they relate to data platforms).

Strong experience with orchestration frameworks (Airflow, Databricks Workflows, or similar), Git, and CI/CD, and with building reliable, observable pipelines.

Solid understanding of data modeling (dimensional modeling, star schemas, medallion architecture) and experience building semantic layers or metric definitions.

Experience inheriting, refactoring, and improving existing data models and pipelines, with an eye toward reliability, maintainability, and backward compatibility for downstream consumers.

Experience using AI/LLM coding assistants (for example, GitHub Copilot, Cursor, or Claude Code), with a disciplined approach to reviewing, testing, and owning generated code rather than accepting i