Senior Data Engineer (Remote)

About The RoleAs our first dedicated Data Engineer, you will build and own the data foundation that powers analytics, reporting, and decision-making across the organization. This is a hands-on role where you'll design the dimensional model, own the pipelines that feed it, and establish the standards our data practice is built on.You won't be starting from zero or working alone. You'll join a data team with direct analytics experience, and partner closely with engineering and other technical teams who have built and run what we have today. There's real institutional knowledge here to draw on. What's been missing is someone whose focus is turning it into a single, well-modeled foundation the whole company can rely on.You will work cross-functionally to understand how data is generated and used, and translate those needs into scalable models and structured reporting layers. A central part of the work is identity resolution: building the spine that reliably connects the same entity as it appears across systems that each have their own identifiers and lifecycles.This role is ideal for someone who enjoys owning data systems end to end — from ingestion and transformation through modeling, governance, and performance — and who wants the autonomy to design a warehouse properly, with colleagues who can help you understand the business behind the data.What You’ll DoDesign and build a conformed, Kimball-style dimensional model across our operational, behavioral, and transactional dataOwn ingestion end to end, including capturing change over time from sources that don't preserve history nativelyConsolidate transformation logic that currently lives in more than one place into a single governed, tested layerImplement and manage our data warehouse and transformation layer, taking ownership of the pipelines that move data from our operational systems into itEstablish foundational best practices for data modeling, documentation, testing, and governanceImprove data reliability, quality, and accessibility across systemsCollaborate with analysts and business stakeholders to support evolving data needsEncode business metric definitions once, so that reporting stops drifting across teamsMonitor and optimize performance and cost efficiency across pipelines, storage, and warehouse queriesYou're a Good Fit If YouHave 5+ years of experience specifically in data engineering, analytics engineering, or a closely related role, including having built and owned a dimensional model in productionHave strong proficiency in SQL, with experience across document-based operational databases (e.g., MongoDB) and analytical data warehouses (e.g., BigQuery, Snowflake, Redshift, or similar)Have experience building fact and dimension tables using star schema principles to support reporting and data marts, with a clear point of view on grain, conformed dimensions, and slowly-changing dimensionsHave hands-on experience with modern transformation and modeling frameworks (e.g., dbt, Dataform, or similar), including managing transformation layers within a warehouse environment with version control, testing, and CIHave built and maintained reliable ETL/ELT pipelines that transform raw application data into structured, analytics-ready datasetsHave worked with orchestration tooling (e.g., Airflow, Dagster, Prefect, or similar) and think in terms of dependencies, retries, and backfillsHave experience with data ingestion or event streaming platforms (e.g., RudderStack, Segment, Pub/Sub, or similar) and ensuring consistent, reliable upstream data flows, including identity stitching across web and mobileHave a solid understanding of data modeling best practices, including schema design, dimensional modeling, and performance considerationsHave a track record of inheriting and operating systems you didn't buildHave a strong focus on data quality, validation, and governance, with the ability to identify and resolve inconsistenciesHave an understanding of performance optimization across pipelines, storage, and warehouse queriesCan explain technical tradeoffs clearly to non-engineersAre comfortable operating in a growing environment where you both execute technically and help shape our data architecture standardsNice to haveChange data capture patterns from operational databasesSubscription billing data — proration, refunds, failed payments, trialsExperience with distributed processing frameworks (e.g., Spark, Beam, Dataflow)Experience as a first or early data hireKey ResponsibilitiesDesign, build, and maintain reliable data pipelines that transform operational data into structured, analytics-ready datasetsDesign and maintain the dimensional model — dimensions, facts, and bridge tables with clearly defined grainDevelop and maintain scalable data models and data marts to support reporting and business analysisManage and optimize data ingestion and event workflows to ensure consistent, high-quality upstream data flowsImplement and manage transformation processes that structure raw data for analytics useImplement orchestration, testing, freshness monitoring, and alerting so that data issues are caught before stakeholders encounter themBuild and maintain change capture or snapshotting to support historical reporting and slowly-changing dimensionsImprove data freshness — moving our core operational data from batch refreshes toward near-real-time availability, and establishing freshness SLAs stakeholders can rely onEnsure strong standards for data quality, validation, and consistency across systemsDocument models and definitions so analysts and stakeholders can self-serve with confidenceMonitor and optimize performance, reliability, and cost efficiency within the analytics environmentPartner cross-functionally to translate business requirements into scalable data solutionsProactively improve our data systems so they remain structured, consistent, and scalable as the organization grows