About the role

Senior Data Engineer at DraftKings designing and scaling modern data infrastructure for analytics. Collaborating with cross-functional teams to solve technical problems and drive innovation.

Responsibilities

Build scalable, high-performance data infrastructure that transforms raw and historical data into trusted, actionable insights.
Design, develop, and maintain real-time and batch data pipelines using technologies such as Kafka, Databricks, and Snowflake.
Develop reliable data solutions that power business-critical reporting, analytics, and platform insights.
Build AI-powered data experiences that enable teammates to explore and analyze data using natural language, extending beyond text-to-SQL to support intelligent, autonomous workflows.
Partner with Engineering, Analytics, and cross-functional stakeholders to translate complex business requirements into scalable, analytics-ready datasets.
Design and optimize modern data models, including Star Schema and Data Vault methodologies, while championing data quality, governance, and observability.
Drive best practices for data engineering, streaming, transformation, and platform performance while mentoring teammates and contributing to the continued growth of the Data Engineering Team.
Collaborate in an agile environment, delivering scalable solutions that support evolving business and platform needs.

A Bachelor's Degree in Computer Science or a related field, or an equivalent combination of education, training, and professional experience.
At least 3 years of experience designing, building, and maintaining scalable data platforms and distributed data systems.
Strong proficiency in Python and SQL, with hands-on experience building real-time data streaming solutions.
Experience working with Snowflake, Databricks, Kafka, and modern cloud-based data platforms.
Experience with cloud infrastructure, preferably Amazon Web Services (AWS), and familiarity with infrastructure and DevOps tools such as Terraform, PagerDuty, and Datadog.
Deep understanding of data warehousing, data lakes, ETL frameworks, and modern data modeling techniques, including Star Schema and Data Vault.
Experience working with NoSQL databases such as MongoDB or DynamoDB; familiarity with Sigma or similar business intelligence tools is a plus.
Hands-on experience using AI-assisted engineering tools such as Claude Code, Codex, or similar technologies to build agents, automate workflows, and accelerate software development.
Strong communication and collaboration skills, with the ability to thrive in a fast-paced, cross-functional environment while mentoring and supporting other engineers.