Senior Data Engineer

This is a fully remote position, open to applicants in North America.

📋 Description

• Develop and take ownership of the data infrastructure that supports analytics, reporting, and organizational decision-making.

• Design and create a conformed, Kimball-style dimensional model encompassing operational, behavioral, and transactional data.

• Manage the entire ingestion process, including capturing historical changes from sources that do not inherently maintain history.

• Streamline transformation logic into a single governed and validated layer.

• Implement and oversee the data warehouse and transformation layer.

• Construct and sustain dependable pipelines that convert operational data into structured, analytics-ready datasets.

• Design and uphold dimensions, facts, and bridge tables with well-defined grain.

• Create scalable data models and data marts for reporting and business analysis purposes.

• Oversee and enhance data ingestion and event workflows.

• Implement orchestration, testing, monitoring of freshness, and alerting mechanisms.

• Develop and maintain change capture or snapshotting for historical reporting and slowly-changing dimensions.

• Enhance data freshness from batch refreshes towards near-real-time availability, establishing freshness SLAs.

• Set standards for data modeling, documentation, testing, governance, data quality, validation, and consistency.

• Encode definitions of business metrics to prevent discrepancies in reporting across teams.

• Document models and definitions to facilitate self-service for analysts and stakeholders.

• Monitor and optimize pipeline performance, storage, warehouse queries, reliability, and cost efficiency.

• Collaborate with analysts, business stakeholders, engineering, and technical teams to convert requirements into scalable data solutions.

• Proactively enhance data systems as the organization expands.

⛳️ Requirements

• Over 5 years of experience specifically in data engineering, analytics engineering, or a closely related role, including experience in building and owning a dimensional model in production.

• Strong expertise in SQL.

• Experience with document-based operational databases like MongoDB and analytical data warehouses such as BigQuery, Snowflake, Redshift, or similar platforms.

• Proficient in building fact and dimension tables utilizing star schema principles, with knowledge of grain, conformed dimensions, and slowly-changing dimensions.

• Practical experience with modern transformation and modeling frameworks like dbt, Dataform, or similar tools.

• Experience in managing warehouse transformation layers with version control, testing, and continuous integration.

• Proven ability to build and maintain reliable ETL/ELT pipelines.

• Familiarity with orchestration tools such as Airflow, Dagster, Prefect, or similar solutions.

• Experience with data ingestion or event streaming platforms such as RudderStack, Segment, Pub/Sub, or similar.

• Competency in ensuring reliable upstream data flows, including identity stitching across web and mobile platforms.

• Solid understanding of data modeling, schema design, dimensional modeling, and performance considerations.

• Proven track record of inheriting and managing systems that were not originally built by you.

• Strong emphasis on data quality, validation, and governance.

• Understanding of performance optimization across pipelines, storage, and warehouse queries.

• Ability to communicate technical trade-offs clearly to non-engineers.

• Comfortable executing technical tasks and shaping data architecture standards within a growing environment.

🏝️ Benefits

• Competitive salary and performance-based incentives.

• Comprehensive health, dental, and vision insurance.

• Opportunities for professional development and growth.

• Flexible work hours and remote work options.

• Supportive and collaborative work environment.