Role Description The Swish Analytics team is seeking Data Engineers based in Europe to have a direct impact on the infrastructure and delivery of our core consumer and enterprise data offerings as well as helping support our coverage of non-US sports. We’re a team passionate about accurate predictions and real-time data, and hope you find satisfaction in building new products with the latest and greatest technologies. This is a remote position. Duties - Support production systems and help triage issues during live sporting events - Architect low-latency, real-time analytics systems including raw data collection, feature development and endpoint production - Build new sports betting data products and predictions offerings - Integrate large and complex real-time datasets into new consumer and enterprise products - Develop production-level predictive analytics into enterprise-grade APIs - Contribute to the design and implementation of new, fully-automated sports data delivery frameworks Qualifications - BS/BA degree in Mathematics, Computer Science, or related STEM field - Minimum of 4+ years of demonstrated experience writing production level code (Python) - Proficiency in Python and SQL (preferably MySQL) - Demonstrated experience with Airflow - Demonstrated experience with Kubernetes - Experience building end-to-end ETL pipelines - Experience utilizing REST APIs - Experience with version control (git), continuous integration and deployment, shell scripting, and cloud-computing infrastructures (AWS) - Experience with web scraping and cleaning unstructured data - Knowledge of data science and machine learning concepts - Professional working experience with MLB or NBA data Requirements - Starting at $145,000 - DOE Benefits - Swish Analytics is an Equal Opportunity Employer. - All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. - The position responsibilities are not limited to the responsibilities outlined above and are subject to change. - At the employer’s discretion, this position may require successful completion of background and reference checks.

• Develop Control Tower reporting: Build and maintain daily, weekly, and monthly reporting for escalations, performance metrics, customer-level insights, and Control Tower operations. • Support customer-facing materials: Prepare reporting and analysis for MBRs, QBRs, white glove programs, and other customer touchpoints, ensuring accuracy and clear storytelling of operational performance. • Enable real-time monitoring: Partner with Control Tower Managers to support escalation tracking, accurate data capture, timely updates, and active issue support through relevant operational insights. • Analyze customer performance: Review performance for top customers, growth accounts, churn-risk customers, and high-priority initiatives to identify trends, risks, and improvement opportunities. • Maintain reporting accuracy: Ensure consistency and accuracy across dashboards, reports, and customer-facing materials. • Improve reporting processes: Identify data gaps and opportunities to strengthen Control Tower reporting, monitoring, and visibility. • Surface trends and root causes: Use available data to identify recurring issues, operational trends, root causes, risks, and opportunities for Control Tower leadership.

• Gather and clarify requirements from analytics, finance, and business stakeholders. • Design and evolve DWH architecture, including ODS and Data Marts, with audit requirements in mind. • Develop and maintain financial data pipelines. • Design, implement, and support incremental data processing pipelines. • Develop and optimize data marts in cloud data warehouse environments. • Analyze source systems and design end-to-end data flows. • Maintain and enhance workflow orchestration processes. • Participate in testing, data validation, and release activities. • Investigate data quality issues and financial discrepancies. • Maintain technical documentation required for audit and data governance processes.

• Drive new enterprise data bookings through direct sales into capital markets institutions. • Structure and negotiate complex data licensing agreements (national, regional, API, Snowflake, bulk feeds). • Engage with CIOs, Heads of Data, Portfolio Managers, Risk Teams, and Investment Committees. • Position LightBox as the identity backbone for institutional property and participant intelligence. • Navigate procurement, legal, and data governance review processes. • Collaborate cross-functionally with Product, Legal, Revenue Operations, and Customer Success. • Develop territory strategies and named account plans. • Identify upsell opportunities tied to ownership intelligence, transaction history, hazard overlays, infrastructure context, and derived insights. • Build multi-threaded relationships within large enterprise accounts.