Tools We Use:
- Python (Airflow)
- SQL (Redshift, Postgres, SQL Server)
- Segment & Stitch
- AWS (Redshift, Glue, MWAA, S3, RDS)
- Design, develop, and deploy reliable and scalable data solutions from data ingestion to ready-for-analysis datasets
- Maintain and optimize data pipelines, data models and data warehouse for performance and scalability
- Identify gaps in tech stack, generate architecture recommendations and implement them
- Guide, improve and implement best practices and standards for code maintainability and performance
- Establish testing standards and design automated mechanisms to monitor and improve data integrity across all data systems
- Partner with product analysts, data analysts, data scientists, and the business to understand the underlying business needs to provide meaningful data solutions
What Skills We’re Looking For:
- Demonstrated hands-on experience in analytics engineering, data engineering, or a backend software development role building data processing systems w/ production quality code
- Experience architecting, designing and building robust and maintainable data systems
- Demonstrably deep understanding of SQL (dbt preferred) and analytical data warehouses (Redshift preferred)
- Must have hands-on experience with Python-based data pipeline tools (Airflow preferred)
- Strong analytical skills to QA data, identify gaps and determine the root cause of issues
- Proficiency in AWS tooling, ideally Redshift Administration, AWS Glue, S3, and RDS
- Bonus points for experience in Salesforce, Google Workspace, Docker, Luigi, Kafka, Spark
Who you are:
- An initiative taker who’s able to solve ambiguous problems from inception to completion
- An open communicator who can speak to audiences of varying technical backgrounds
- A highly collaborative person with a mentorship mindset
- Someone who knows how to balance business and technical needs
- Someone who is excited about all things data and eager to learn
Commitment to Diversity and Equal Employment Opportunity
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