Structure and optimize data for powerful analytics & reporting by designing, building, and maintaining data pipelines and systems to ensure the accuracy, availability, consistency, and usability of data for analysis.
Requirements
- Extensive experience with dbt for building, testing, and maintaining reliable data models and transformations.
- Proficiency with Snowflake and building scalable data warehouses.
- Expertise in Kimball-style dimensional modeling (star schema, fact and dimension tables, slowly changing dimensions).
- Strong SQL skills for querying, automation, optimization and data validation.
- Proficient in Python for scripting and automation.
- Familiarity with AWS cloud tools (Lambda, SQS, Athena, EC2, Kinesis) and cloud-based data pipelines.
- Understanding of event and web analytics data from tools like Amplitude and Google Analytics.
Responsibilities
- Lead the design, development, and maintenance of scalable dbt models, ensuring modularity and efficiency.
- Apply Kimball-style dimensional modeling techniques (star schema, slowly changing dimensions, fact and dimension tables) to structure data optimally for analysis and reporting.
- Explore, analyze, and validate data to ensure accuracy, consistency, and completeness of dbt models.
- Implement and maintain automated dbt tests to ensure data quality, including checks for anomalies, nulls, duplicates, and referential integrity.
- Investigate and troubleshoot data anomalies, pipeline errors, and model discrepancies, collaborating with teams to resolve issues promptly.
- Partner closely with data analysts to build reliable reporting models and ensure the data feeding reports is accurate, actionable, and aligned with business needs.
- Ensure the availability, consistency, and accessibility of data for analytics teams working with Tableau, PowerBI, Amplitude, Google Analytics, and other BI tools.
Other
- 4+ years of experience as a Data Engineer or in a similar role.
- Bachelor's degree in a related discipline and 4 years' experience in a related field.
- Strong knowledge of data quality, validation, and governance best practices.
- Experience collaborating with data analysts to build accurate, actionable reporting models.
- Experience with real-time data streams and troubleshooting data pipeline issues.
