Zencastr - Senior Data Engineer

• Design and build a conformed, Kimball-style dimensional model across our operational, behavioral, and transactional data • Own ingestion end to end, including capturing change over time from sources that don't preserve history natively • Consolidate transformation logic that currently lives in more than one place into a single governed, tested layer • Implement and manage our data warehouse and transformation layer, taking ownership of the pipelines that move data from our operational systems into it • Establish foundational best practices for data modeling, documentation, testing, and governance • Improve data reliability, quality, and accessibility across systems • Collaborate with analysts and business stakeholders to support evolving data needs • Encode business metric definitions once, so that reporting stops drifting across teams • Monitor and optimize performance and cost efficiency across pipelines, storage, and warehouse queries • You're a Good Fit If You • Have 5+ years of experience specifically in data engineering, analytics engineering, or a closely related role, including having built and owned a dimensional model in production • Have 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 dimensions • Have 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 CI • Have built and maintained reliable ETL/ELT pipelines that transform raw application data into structured, analytics-ready datasets • Have worked with orchestration tooling (e.g., Airflow, Dagster, Prefect, or similar) and think in terms of dependencies, retries, and backfills • Have 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 mobile • Have a solid understanding of data modeling best practices, including schema design, dimensional modeling, and performance considerations • Have a track record of inheriting and operating systems you didn't build • Have a strong focus on data quality, validation, and governance, with the ability to identify and resolve inconsistencies • Have an understanding of performance optimization across pipelines, storage, and warehouse queries • Can explain technical tradeoffs clearly to non-engineers • Are comfortable operating in a growing environment where you both execute technically and help shape our data architecture standards • Design, build, and maintain reliable data pipelines that transform operational data into structured, analytics-ready datasets • Design and maintain the dimensional model — dimensions, facts, and bridge tables with clearly defined grain • Develop and maintain scalable data models and data marts to support reporting and business analysis • Manage and optimize data ingestion and event workflows to ensure consistent, high-quality upstream data flows • Implement and manage transformation processes that structure raw data for analytics use • Implement orchestration, testing, freshness monitoring, and alerting so that data issues are caught before stakeholders encounter them • Build and maintain change capture or snapshotting to support historical reporting and slowly-changing dimensions • Improve data freshness — moving our core operational data from batch refreshes toward near-real-time availability, and establishing freshness SLAs stakeholders can rely on • Ensure strong standards for data quality, validation, and consistency across systems • Document models and definitions so analysts and stakeholders can self-serve with confidence • Monitor and optimize performance, reliability, and cost efficiency within the analytics environment • Partner cross-functionally to translate business requirements into scalable data solutions • Proactively improve our data systems so they remain structured, consistent, and scalable as the organization grows