Data Engineer

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. The Senior Database Engineer will have a direct impact on the data infrastructure of core consumer and enterprise data offerings, focusing on building scalable and efficient data processing systems using Python and cloud platforms.

Responsibilities:

• Proficiency in Python: Expertise in writing scalable, efficient, and testable code using Python for data processing, automation, and building back-end components. Familiarity with popular Python libraries is a plus
• Cloud Platforms: Proficiency in cloud services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, including knowledge of their specific data tools (e.g., Redshift, S3, Azure Data Factory, BigQuery)
• 24/7 monitoring, alerting, and incident response of enterprise database architecture
• Developing KPI's, SLA’s, SLO’s and strategy to achieve and improve database stability
• Backup/recovery testing and disaster recovery planning
• Developing performance testing strategy
• Query performance analysis and optimization
• Implement and maintain RDS Proxy and connection management strategies
• Manage database environments from production, data streams, to 'Big Data' analytics
• Create and maintain policy runbooks and documentation for company use
• Database security, access control, and compliance
• Working with MySQL/Postgres, Redshift, Kafka, Athena, Redis/Valkey, S3 and similar
• Own schema migration tooling and process development

Requirements:

• Proficiency in Python: Expertise in writing scalable, efficient, and testable code using Python for data processing, automation, and building back-end components. Familiarity with popular Python libraries is a plus
• Cloud Platforms: Proficiency in cloud services such as Amazon Web Services (AWS), Google Cloud Platform (GCP), or Microsoft Azure, including knowledge of their specific data tools (e.g., Redshift, S3, Azure Data Factory, BigQuery)
• 24/7 monitoring, alerting, and incident response of enterprise database architecture
• Developing KPI's, SLA's, SLO's and strategy to achieve and improve database stability
• Backup/recovery testing and disaster recovery planning
• Developing performance testing strategy
• Query performance analysis and optimization
• Implement and maintain RDS Proxy and connection management strategies
• Manage database environments from production, data streams, to 'Big Data' analytics
• Create and maintain policy runbooks and documentation for company use
• Database security, access control, and compliance
• Working with MySQL/Postgres, Redshift, Kafka, Athena, Redis/Valkey, S3 and similar
• Own schema migration tooling and process development
• Operational Excellence: Monitoring (CloudWatch, Performance Insights, Datadog), alerting, on-call experience
• MySQL Operations: Deep knowledge of RDS operational functions, performance tuning (indexes, query optimization, explain plans) Backup/restore, point-in-time recovery, replication troubleshooting
• SQL Mastery: Proficiently writing and understanding complex SQL queries, including joins, subqueries, aggregations, and window functions
• Database Design Principles: Knowledge of normalization, denormalization, and how table structure impacts query performance
• Indexing: Understanding different index types (B-tree, hash, clustered, non-clustered) and their appropriate use for optimizing search and retrieval
• Automation: Software development for operational tasks
• AWS: RDS operations, Authentication, Authorization
• Incident Management: Root cause analysis, postmortem creation, Run book development
• Performance testing tools (sysbench, HammerDB)
• Execution Plan Analysis: Ability to read and interpret query execution plans to identify bottlenecks, such as full table scans, inefficient joins, or missing indexes
• Identifying Resource Bottlenecks: Pinpointing where queries are consuming excessive CPU, I/O, connections, or memory