Data Engineer

FanDuel•Atlanta, GA

2h•$116,000 - $145,000•Hybrid

About The Position

Our roster has an opening with your name on it. We are looking for a Data Engineer to join our growing data engineering team and help build the pipelines and infrastructure that power analytics, machine learning, and business decision-making across the company. In this role, you’ll own the design, development, and maintenance of reliable data systems while collaborating with stakeholders to support high-impact data use cases. The ideal candidate is a strong technical contributor who enjoys working with data at scale, solving practical problems, independently executing on projects, and continuously learning in a fast-paced environment. If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you. In addition to the specific responsibilities outlined above, employees may be required to perform other such duties as assigned by the Company. This ensures operational flexibility and allows the Company to meet evolving business needs.

Requirements

3+ years of experience in data engineering, analytics engineering, or software engineering with a focus on data.
Strong SQL skills and familiarity with at least one programming language (e.g., Python, Java, or Scala).
Hands-on experience with modern data tools such as Databricks, Airflow, dbt, Spark, or Kafka.
Understanding of data modeling concepts, data warehousing, and ETL/ELT best practices.
Experience working with cloud-based data platforms (AWS, GCP, or Azure).

Nice To Haves

Experience supporting BI, analytics, or data science teams.
Familiarity with version control, CI/CD, and collaborative development workflows.
Exposure to data governance, privacy, or compliance practices.
Eagerness to learn new technologies and contribute to the growth of the team.

Responsibilities

Design, build, and maintain scalable batch and streaming data pipelines to support analytics and business operations.
Write clean, efficient, and well-documented code using tools like Python, SQL, and Spark.
Ensure data is reliable, accurate, and delivered in a timely manner.
Lead projects to build or improve well-scoped data products.
Work with data analysts, data scientists, and product managers to understand and clarify ambiguous requirements to deliver actionable data solutions.
Translate business questions into engineering tasks and contribute to technical planning.
Participate in code reviews, sprint planning, and retrospectives as part of an agile team.
Contribute to team wide best practices.
Monitor data pipelines and troubleshoot issues in a timely, systematic manner.
Driving reliability and continuous improvement of data platforms and pipelines.
Implement data quality checks and contribute to observability and testing practices.
Document data sources, transformations, and architecture decisions to support long-term maintainability.