Analytics Engineer

Swish Analytics•San Francisco, CA

3d•$150,000

About The Position

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and consumer/enterprise clients. The Suspensions team is responsible for the framework, monitoring, and analysis behind how markets are suspended and resumed — from the moment a market opens pregame through live gameplay to close. A few examples of what the team owns: real-time event processing that triggers suspensions off live game state, monitoring and alerting on suspension/resumption latency and failures, tooling that reconstructs and audits what happened during a specific suspension event, and rate/downtime metrics that describe how well suspension coverage is performing across sports and markets. The team works directly in Python and Rust across this stack, and partners closely with data science, trading, and engineering teams whose systems intersect with suspension logic.

Requirements

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)

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