Data Engineer
Blizzard Entertainment
AirflowAWSAzureBigQueryCloudETLGoogle Cloud PlatformOraclePython
Job Level
Mid-LevelSenior
Tech Stack
AirflowAmazon RedshiftBigQueryCloudETLGoogle Cloud PlatformJavaScriptPythonSQLWeb3
About the role
- Thesis is a cryptocurrency venture studio building on Bitcoin since 2014; remote-first company. We seek, fund, and build products/protocols in cryptocurrency and decentralized businesses. Our projects include Mezo (Bitcoin finance app), Keep/Threshold Network, Fold, Taho, Lolli, Embody. Remote-first and experience in crypto. About the Data Engineer role: mid-level Data Engineer with experience in financial services, crypto, or blockchain data. You’ll help expand in-house data capabilities and design pipelines for high-volume, high-integrity financial data. What You’ll Do: Architect complex, real-time data pipelines; Proactively optimize and maintain processes; Collaborate with Data Science; Work closely with on-chain data; Collaborate with cross-functional teams. Requirements: 3–6 years in data engineering; Python and SQL; Data warehousing (Snowflake, BigQuery, Redshift); GTM; Orchestration like Fivetran/Airflow; GCP; Data governance; Crypto data tooling; Data warehousing patterns; Preferred: Looker etc.
Requirements
- 3–6 years in a data engineering role, with at least some experience in DeFi, fintech, or a related field
- Extensive experience with Python and SQL
- Experience with data warehousing solutions (Snowflake, BigQuery, Redshift)
- Strong understanding of Google Tag Manager (familiarity with Data Layer a plus)
- Expertise with orchestration tooling like Fivetran or Airflow, data transformation tools like dbt, and git/Github
- Comprehensive understanding of the Google Cloud Platform, including Cloud SQL, Cloud Functions, and BigQuery
- Familiarity with data governance and compliance standards
- Hands-on experience with blockchain or crypto data, including core tools like Dune or Goldsky
- Knowledge of standard ETL patterns, modern data warehousing ideas such as data mesh or data vaulting, and data quality practices
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