Job Description
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Company Overview
A growing organization is building a strong data foundation to support analytics, reporting, and data-driven decision-making across the business. The organization values scalable data systems, reliable reporting, clear business metrics, and strong data governance.
Key Responsibilities
- Design and build a conformed, Kimball-style dimensional model across operational, behavioral, and transactional data.
- Design, build, and maintain reliable data pipelines that transform operational data into structured, analytics-ready datasets.
- Own data ingestion end to end, including capturing historical changes from sources that do not preserve history natively.
- Design and maintain dimensional models consisting of dimensions, facts, and bridge tables with clearly defined grain.
- Develop and maintain scalable data models and data marts to support reporting and business analysis.
- Consolidate transformation logic into a single governed and tested transformation layer.
- Implement and manage the data warehouse and transformation layer.
- Manage and optimize data ingestion and event workflows to maintain consistent and reliable upstream data flows.
- Implement transformation processes that structure raw data for analytics and reporting.
- Establish foundational best practices for data modeling, documentation, testing, and governance.
- Implement orchestration, testing, freshness monitoring, and alerting to identify data issues before they affect stakeholders.
- Build and maintain change-capture or snapshotting processes to support historical reporting and slowly changing dimensions.
- Improve data freshness by moving core operational data from batch refreshes toward near-real-time availability.
- Establish and maintain freshness SLAs that stakeholders can rely on.
- Improve data reliability, quality, accessibility, and consistency across systems.
- Encode business metric definitions centrally to reduce inconsistencies across reporting teams.
- Document data models and definitions so analysts and stakeholders can confidently self-serve.
- Monitor and optimize performance, reliability, and cost efficiency across pipelines, storage, and warehouse queries.
Required Qualifications
- 5+ years of experience specifically in data engineering, analytics engineering, or a closely related field.
- Experience building and owning dimensional models in production environments.
- Strong proficiency in SQL.
- Experience working with document-based operational databases such as MongoDB.
- Experience working with analytical data warehouses such as BigQuery, Snowflake, Redshift, or similar platforms.
- Experience building fact and dimension tables using star schema principles.
- Strong understanding of data grain, conformed dimensions, and slowly changing dimensions.
- Hands-on experience with modern transformation and modeling frameworks such as dbt, Dataform, or similar technologies.
- Experience managing transformation layers within warehouse environments using version control, testing, and CI.
- Experience building and maintaining reliable ETL/ELT pipelines that transform raw application data into analytics-ready datasets.
- Experience with orchestration tools such as Airflow, Dagster, Prefect, or similar platforms.
- Strong understanding of pipeline dependencies, retries, backfills, and operational reliability.
- Experience with data ingestion or event-streaming platforms such as RudderStack, Segment, Pub/Sub, or similar technologies.
- Experience maintaining consistent and reliable upstream data flows.
- Understanding of identity resolution and identity stitching across web and mobile systems.
- Strong understanding of data modeling, schema design, dimensional modeling, and performance optimization.
- Experience inheriting, operating, and improving systems that were originally built by other teams.
- Strong focus on data quality, validation, consistency, and governance.
- Ability to identify and resolve data inconsistencies across systems.
- Understanding of performance optimization across pipelines, storage, and analytical warehouse queries.
- Ability to clearly communicate technical concepts and tradeoffs to non-technical stakeholders.
- Comfortable working in a growing environment while contributing both technically and architecturally.
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