• Design and deliver relational and non-relational database models, data pipelines, reporting, and visualization solutions while supporting all phases of the analytics development life cycle (ADLC), including requirements gathering, design, development, testing, and deployment. • Develop, maintain, and scale robust ETL/ELT pipelines across HR data sources (e.g., Workday, Greenhouse Recruiting, internal tools such as Arbor), ensuring reliability, performance, and extensibility. • Architect and implement scalable data models optimized for analytical querying and long-term maintainability. • Ensure data quality, integrity, and reliability across all data assets, introducing automation and best practices for monitoring and validation. • Collaborate with People Analytics stakeholders to translate requirements into sound technical solutions and influence longer-term data architecture decisions. • Manage and optimize cloud data warehouse infrastructure (e.g., Snowflake), including performance tuning, cost management, and secure access patterns. • Leverage AI and LLMs to automate data quality checks, enhance metadata management, and extract insights from unstructured HR data. • Stay current on technology best practices and advocate for engineering excellence across the People Analytics team. • Own and manage data governance, security, privacy, and retention standards across all People Analytics systems.

• Designing, developing and programming methods, processes, and systems to consolidate and analyze unstructured, diverse “big data” sources to generate actionable insights and solutions for client services and product enhancement • Interacting with product and service teams to identify questions and issues for data analysis and experiments • Developing and coding software programs, algorithms and automated processes to cleanse, integrate and evaluate large datasets from multiple disparate sources • Identifying meaningful insights from large data and metadata sources; interpreting and communicating insights and findings from analysis and experiments to product, service, and business managers • Working closely with Application Engineering, Product Management, and Operational teams in designing, experimenting-with, and implementing machine learning and analytical systems applied to design information and user behavior • Scaling to large data-sets and validating results will be common tasks for this role • Understanding, organizing, and communicating root causes of problems and successes succinctly

• Support risk and liability management with respect to Sleeper’s DFS operations, conducting data analysis and modeling to identify trends, patterns, and insights. • Develop predictive models and algorithms to better understand Sleeper’s real-time liability, including risk exposure related to specific markets and events. • Conduct data wrangling, processing and cleaning operations, and design processes, as well as tools for monitoring and analyzing performance and accuracy. • Monitor all pricing for real-time odds, active player statistics markets, breaking news, and other relevant information to make real-time market adjustments / suspensions, while also ensuring that data flows from external partners are being recorded timely and accurately. • Build out user risk profiles, staying close to behavior patterns and trends, and collaborate with product and engineering teams to enhance personalization within our DFS ecosystem. • Automate risk and trading processes to scale for growth in users, sports, and markets in support of all game operations, and contribute to research and innovation for this function as a whole.

• Architecture & Development • Design, build, and optimize data pipelines and ETL workflows in Snowflake using Snowpark, Streams/Tasks, and Snowpipe. • Develop scalable data models, Algorithm supporting user 360 views, churn prediction, and recommendation engine inputs. • Lead integration across data sources: MySQL, BigQuery, Redis, Kafka, GCP Storage, and API Gateway. • Implement CI/CD for data pipelines using Git, dbt, and automated testing. • Define data quality checks and auditing pipelines for ingestion and transformation layers. • Leadership & Collaboration • Mentor and guide junior data engineers on data modeling, performance tuning, and Snowflake best practices. • Partner with Data Science, ML, and Backend teams to productionize machine learning features in Snowflake. • Work closely with Legal, Security, and Infrastructure teams to ensure compliance, privacy, and governance of user data (PII). • Collaborate with the Director of Data Platforms and product stakeholders to translate business requirements into technical specifications. • Performance & Scalability • Tune algorithm performance. • Establish data partitioning, clustering, and materialized views for fast query execution. • Build dashboards and monitors for pipeline health, job success, and data latency metrics (e.g., via Looker, Tableau, or Snowsight). • Governance & Best Practices • Establish and enforce naming conventions, data lineage, and metadata standards across schemas. • Lead code reviews, enforce documentation standards, and manage schema versioning. • Contribute to the company’s evolving data mesh and streaming architecture vision.