Senior Data Engineer

• Build and own the data foundation powering analytics, reporting, and organizational decision-making
• Design and build a conformed, Kimball-style dimensional model across operational, behavioral, and transactional data
• Own ingestion end to end, including capturing historical changes from sources that do not preserve history natively
• Consolidate transformation logic into a single governed and tested layer
• Implement and manage the data warehouse and transformation layer
• Build and maintain reliable pipelines transforming operational data into structured, analytics-ready datasets
• Design and maintain dimensions, facts, and bridge tables with clearly defined grain
• Develop scalable data models and data marts for reporting and business analysis
• Manage and optimize data ingestion and event workflows
• Implement orchestration, testing, freshness monitoring, and alerting
• Build and maintain change capture or snapshotting for historical reporting and slowly-changing dimensions
• Improve data freshness from batch refreshes toward near-real-time availability and establish freshness SLAs
• Establish standards for data modeling, documentation, testing, governance, data quality, validation, and consistency
• Encode business metric definitions to prevent reporting drift across teams
• Document models and definitions for analyst and stakeholder self-service
• Monitor and optimize pipeline, storage, warehouse query, performance, reliability, and cost efficiency
• Collaborate with analysts, business stakeholders, engineering, and technical teams to translate requirements into scalable data solutions
• Proactively improve data systems as the organization grows

• 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
• Strong proficiency in SQL
• Experience with document-based operational databases such as MongoDB and analytical data warehouses such as BigQuery, Snowflake, Redshift, or similar
• Experience building fact and dimension tables using star schema principles, with knowledge of grain, conformed dimensions, and slowly-changing dimensions
• Hands-on experience with modern transformation and modeling frameworks such as dbt, Dataform, or similar
• Experience managing warehouse transformation layers with version control, testing, and CI
• Experience building and maintaining reliable ETL/ELT pipelines
• Experience with orchestration tooling such as Airflow, Dagster, Prefect, or similar
• Experience with data ingestion or event streaming platforms such as RudderStack, Segment, Pub/Sub, or similar
• Experience ensuring reliable upstream data flows, including identity stitching across web and mobile
• Solid understanding of data modeling, schema design, dimensional modeling, and performance considerations
• Track record of inheriting and operating systems you did not build
• Strong focus on data quality, validation, and governance
• Understanding of performance optimization across pipelines, storage, and warehouse queries
• Ability to explain technical tradeoffs clearly to non-engineers
• Comfortable executing technically and shaping data architecture standards in a growing environment