Analytics Engineer

Summary

Swish Analytics is a sports analytics, betting, and fantasy startup building predictive sports analytics data products. The Analytics Engineer will investigate production incidents, develop statistical metrics and reporting, and build tooling for the Suspensions team. The role also involves analyzing real-time event-driven data and collaborating with data science, engineering, and trading teams.

Responsibilities

• Investigate individual incidents and requests end-to-end, working directly with raw production data and systems to determine root cause and recommend resolution
• Build and maintain metrics that measure system health and performance over time, using statistical methods as the core analytical approach, while also producing clear descriptive reporting for stakeholders
• Contribute directly to the team's core framework and tooling, making investigation and measurement work more repeatable and less bespoke over time
• Operate independently on ambiguous, partially-scoped problems, identifying the right cross-functional partners (data science, engineering, trading) when a problem crosses team boundaries
• Work with real-time, event-driven data to reconstruct and explain system behavior during live events

Skills

• Bachelor's Degree in Computer Science, Statistics, Data Science, or similar major
• Minimum of 4 years of professional software engineering experience, including production systems
• Minimum of 2 years of experience with Python, including data extraction, wrangling, and analysis
• Minimum of 1 year of experience with Rust in a production environment
• Experience building and maintaining software that runs in production against real-world data — not just prototypes, one-off scripts, or notebook-based analysis
• Strong SQL skills and direct experience working with raw/source data (logs, event streams, production tables)
• Experience taking on open-ended problems with limited upfront direction — figuring out the right questions to ask, who else needs to be involved, and driving the work to a conclusion without needing the problem pre-scoped for you
• Genuine statistical/quantitative reasoning skills — comfortable building rigorous, defensible measures of system behavior
• Experience with event-driven or real-time data systems (e.g., Kafka or comparable)
• Background in analytics engineering, applied statistics, or a hybrid data/software role
• Exposure to sports, sports betting, or trading concepts (helpful, not required)

Qualifications

Must Haves

• Bachelor's Degree in Computer Science, Statistics, Data Science, or similar major
• Minimum of 4 years of professional software engineering experience, including production systems
• Minimum of 2 years of experience with Python, including data extraction, wrangling, and analysis
• Minimum of 1 year of experience with Rust in a production environment
• Experience building and maintaining software that runs in production against real-world data — not just prototypes, one-off scripts, or notebook-based analysis
• Strong SQL skills and direct experience working with raw/source data (logs, event streams, production tables)
• Experience taking on open-ended problems with limited upfront direction — figuring out the right questions to ask, who else needs to be involved, and driving the work to a conclusion without needing the problem pre-scoped for you
• Genuine statistical/quantitative reasoning skills — comfortable building rigorous, defensible measures of system behavior

Nice to Haves

• Experience with event-driven or real-time data systems (e.g., Kafka or comparable)
• Background in analytics engineering, applied statistics, or a hybrid data/software role
• Exposure to sports, sports betting, or trading concepts (helpful, not required)

Benefits

• Fully Remote