• Owning the design and development of robust dbt Core models that transform raw data into trusted, analytics‑ready datasets in Snowflake • Architecting scalable, high‑performance data models that support enterprise reporting, analytics, and AI use cases • Translating complex business and analytical requirements into efficient, well‑structured ELT solutions through close collaboration with BI, analytics, and business stakeholders • Embedding best practices in data quality, testing, documentation, and lineage to ensure transparency, reliability, and trust in our data ecosystem • Leveraging Python to support automation, data validation, orchestration, and performance monitoring across ELT pipelines • Monitoring, tuning, and optimizing Snowflake query performance and cost efficiency • Leading technical design discussions and contributing hands‑on to critical data initiatives • Serving as a technical lead and mentor, guiding other engineers and elevating standards across the full data transformation lifecycle • Providing thought leadership on modern data transformation patterns, tooling, and architecture to help shape enterprise data strategy • Supporting data governance and metadata enrichment initiatives in alignment with broader enterprise data goals

• You'll be the first person at LawnStarter dedicated to data governance - the owner of whether our data can be trusted. • That means the quality and freshness of our source data, pipelines, and reports; the definitions behind our metrics; the standards behind our Segment event tracking; the health of our Lightdash workspace; the data feeding our machine learning models; and the security of the data itself. • This is a hands-on role. You'll work solo at first, with the Analytics team around you but nobody under you - building automation, writing checks, fixing what's broken, and putting processes in place that scale past you. If the scope grows the way we expect, this becomes the foundation of a team you'd build. • Data quality and freshness - automated monitoring across source data, pipelines, and reports; catching upstream schema and source changes before they break anything downstream; running incidents to resolution when they happen. • Data lineage and impact analysis - a living map from production source to warehouse model to dashboard, and the process that uses it: when a production change is proposed, its downstream impact on pipelines, metrics, and reports gets assessed before it ships, not discovered after. The end-state is data contracts with engineering, so breaking changes get caught in their workflow, not ours. • Lightdash - administration, workspace structure, permissions, and the rollout itself. Your job is to give the company self-serve autonomy while keeping the workspace tidy enough that people can find and trust what's there. Enablement is part of the deal - people follow standards they've been taught - and so is keeping queries fast and warehouse costs sane. • The semantic layer - we just shipped it for our most critical metrics: one governed definition per metric, in code. You'll extend definition and mapping to the rest and guard the layer against uncontrolled growth as it scales. • Event tracking governance - our governed Segment event catalog: reviewing new events against its standards, keeping it matched to what production actually sends, and evolving the guardrails (naming, property dictionary, drift detection) as tracking grows. • AI data readiness - AI agents query our warehouse every day through Brain, our internal AI toolkit. You'll govern what data AI tools can access and keep the warehouse AI-legible: documented, consistent, and safe for an agent to query and get the right answer. • Data security and privacy - access controls, PII handling and retention under US state privacy laws, and periodic reviews of who - and which AI tools - can see what. • The governance system itself - the documentation, ownership models, and review loops that keep all of the above running without heroics.

• Design and implement scalable data platforms using Snowflake, Databricks, Delta Lake, and cloud technologies. • Build batch and real-time data pipelines using PySpark, Kafka, and Spark Structured Streaming. • Develop AI-ready data architectures supporting analytics, ML, LLMs, and RAG applications. • Design semantic models, data governance, metadata, and data lineage solutions. • Implement vector databases, embedding pipelines, and retrieval solutions for AI applications. • Build and manage ML/LLMOps pipelines, model deployment, monitoring, and CI/CD. • Ensure data security, RBAC, compliance, and governance across the platform. • Mentor engineering teams and define architecture best practices.

• Assist in developing and maintaining basic data ingestion and transformation pipelines (ETL/ELT) using PySpark and SQL. • Help monitor data pipelines and implement basic checks to ensure data reliability for internal consumers (such as Data Scientists). • Learn and assist in automating pipeline testing and deployment processes. • Work alongside data scientists and software engineers to understand and support integrated data flows. • Assist in documenting data schemas, pipeline architectures, and metadata cataloging.