The engineering team at Chainalysis is looking for a Data Engineer to focus on building scalable data tooling and data science infrastructure;  help us democratize data access across our growing enterprise! Our ideal candidate is comfortable working as an individual contributor in a fast-paced environment with a lot of ambiguity. You will be responsible for reducing friction between business teams hungry for data assets and our current data warehouse/data infrastructure solutions. This will include but is not limited to optimizing our AWS RedShift clusters, building new ETL pipelines, maintaining and managing BI tools, and upgrading our data science capabilities. You can perform these actions while maintaining SOC2/GDPR compliance and well architected frameworks.

In one year you’ll know you were successful if...

• We have increased usage of our Data Warehouse while controlling overall cost
• Sentiment from the business teams is that data is much easier to access, reliable and of high quality and we can prove that with metrics
• We have successfully migrated our data science environment from our data center into AWS
• You have built tools and mechanisms for consolidating and updating data regularly to make it easily accessible to all stakeholders in the company
• You’ve treated our internal data as a product and helped to scale the company’s analytics platforms, trained staff to leverage BI tools, and provided your colleagues with easier access to data assets

A background like this helps: 

• Educational background in Computer Science, Engineering, Math, Statistics, Economics or a quantitative discipline preferred (but not required)
• You’ve been successful building Data Warehouse/BI and Data Science enablement solutions on AWS or similar platforms. 
• Advanced skills in SQL, ETL, DB tuning and optimization (e.g. Redshift, PostgreSQL, Athena/Glue)
• Infrastructure as code (Terraform) and CI/CD (Docker, EKS, Helm) 
• Knowledge and experience with data pipeline technologies, data modeling (star schemas, snowflake, aggregation tables), basic data architectures (compute and reporting clusters)
• Experience in inventing new metrics, conducting root cause analysis, testing hypotheses and developing reporting/visualization solutions (e.g. Tableau, AWS Quicksight)
• Ability to work across the organization to identify high-friction areas or data gaps
• Excellent organizational skills including prioritization of multiple concurrent projects while still delivering timely and accurate results.
• "Scrappy", self-sufficient, and a strong bias for action
• An ownership mindset - acts and makes decisions on behalf of the company, not just the team. 

The following would differentiate you:

• Understanding of blockchain data structures and interfaces (BTC, ETH, and others) 
• Experience with technologies such as Airflow, Kafka, Hadoop or Spark