• Design and build robust, idempotent data pipelines from scratch utilizing a modern data stack. • Design star and snowflake schemas, writing precise, grain-aware SQL to construct scalable data marts. • Write production-grade, unit-tested Python code at the module level, adhering to strong engineering disciplines such as type hinting and testing. • Build and test dbt models across staging, intermediate, and mart layers while managing overall project structure. • Author and deploy jobs using Databricks Asset Bundles (DAB) following documented architectural patterns. • Implement rigorous data quality checks at source, intermediate, and destination layers to prevent silent drops of nulls or duplicates. • Maintain data governance through comprehensive dbt tests and strict documentation-at-merge-time discipline. • Operate securely within a multi-repository architecture, utilizing service principals and ensuring zero personal credentials in production deployments. • Run cross-repository exposure checks prior to merging schema-breaking changes. • Own data pipelines end-to-end, making key technical design decisions and mentoring mid-level engineers through substantive code reviews. • Define overarching technical direction across core data systems, including modeling standards, branching strategies, observability thresholds, and secret management policies. • Act as a technical leader to unblock the team and actively participate in hiring panels to scale the engineering organization.

• Design and build robust, idempotent data pipelines from scratch utilizing a modern data stack. • Design star and snowflake schemas, writing precise, grain-aware SQL to construct scalable data marts. • Write production-grade, unit-tested Python code at the module level, adhering to strong engineering disciplines such as type hinting and testing. • Build and test dbt models across staging, intermediate, and mart layers while managing overall project structure. • Author and deploy jobs using Databricks Asset Bundles (DAB) following documented architectural patterns. • Implement rigorous data quality checks at source, intermediate, and destination layers to prevent silent drops of nulls or duplicates. • Maintain data governance through comprehensive dbt tests and strict documentation-at-merge-time discipline. • Operate securely within a multi-repository architecture, utilizing service principals and ensuring zero personal credentials in production deployments. • Run cross-repository exposure checks prior to merging schema-breaking changes. • Own data pipelines end-to-end, making key technical design decisions and mentoring mid-level engineers through substantive code reviews. • Define overarching technical direction across core data systems, including modeling standards, branching strategies, observability thresholds, and secret management policies. • Act as a technical leader to unblock the team and actively participate in hiring panels to scale the engineering organization.

• Design and build robust, idempotent data pipelines from scratch utilizing a modern data stack. • Design star and snowflake schemas, writing precise, grain-aware SQL to construct scalable data marts. • Write production-grade, unit-tested Python code at the module level, adhering to strong engineering disciplines such as type hinting and testing. • Build and test dbt models across staging, intermediate, and mart layers while managing overall project structure. • Author and deploy jobs using Databricks Asset Bundles (DAB) following documented architectural patterns. • Implement rigorous data quality checks at source, intermediate, and destination layers to prevent silent drops of nulls or duplicates. • Maintain data governance through comprehensive dbt tests and strict documentation-at-merge-time discipline. • Operate securely within a multi-repository architecture, utilizing service principals and ensuring zero personal credentials in production deployments. • Run cross-repository exposure checks prior to merging schema-breaking changes. • Own data pipelines end-to-end, making key technical design decisions and mentoring mid-level engineers through substantive code reviews. • Define overarching technical direction across core data systems, including modeling standards, branching strategies, observability thresholds, and secret management policies. • Act as a technical leader to unblock the team and actively participate in hiring panels to scale the engineering organization.

• Define and drive the technical roadmap across data platform, analytics engineering, and ads data infrastructure. Set the architectural vision for how data is ingested, transformed, modeled, and served at Life360. • Own the analytics engineering strategy end-to-end: dbt project structure, data modeling standards (dimensional, OBT, and semantic layer), testing and documentation practices, and the development workflow that analytics engineers use daily. • Oversee the data platform: Databricks infrastructure, compute optimization, pipeline orchestration, data lake architecture, and the reliability/observability stack that keeps it all running at consumer scale. • Drive toward a self-serve data experience where analysts and data scientists can answer their own questions without engineering bottlenecks—this is the outcome that ties platform and analytics engineering together. • Make strategic build vs. buy decisions across the data stack and manage vendor relationships (Snowflake, Databricks, Amplitude, and related tooling). • Drive data quality, governance, and documentation standards that make data trustworthy and self-service across the company. • Bring an AI native approach to data engineering: leverage AI tools to accelerate development cycles, evaluate AI-powered data quality and anomaly detection solutions, and ensure our data infrastructure supports ML/AI workloads and experimentation at scale. • Stay current on emerging technologies in the data and AI space and make pragmatic decisions about adoption—knowing when a new tool solves a real problem vs. when it’s a distraction.