Data Engineer

FanDuel•Atlanta, GA

About The Position

FanDuel is seeking a Data Support Engineer to advance its data capabilities and ensure the reliability and performance of critical data pipelines and infrastructure. The role is part of the Data Support team and supports data consumers across the company by maintaining healthy data infrastructure and helping deliver reliable, timely, and usable data. It combines technical troubleshooting with proactive monitoring in an agile environment alongside colleagues in data engineering, machine learning, and related areas. The role participates in an on-call pager rotation to provide 24/7 coverage for critical data issues. Employees may also be asked to perform other duties as assigned to support evolving business needs.

Requirements

At least 3 years of data engineering experience focused on operational support.
Advanced SQL skills and demonstrated query optimization experience.
Strong problem-solving skills and ability to take ownership and accountability for issues through resolution.
Proficiency in Python programming for automation and data processing.
Experience with cloud data platforms such as AWS, Databricks, and Spark.
Understanding of data warehousing and ETL/ELT processing.
Experience with dimensional data modeling, batch data ingestion, and real-time technologies such as Kafka or Kinesis.
Working knowledge of orchestration and monitoring tools such as Airflow, Datadog, Databand, DataFold, Monte Carlo, Prometheus, and Grafana.
Strong incident management and root-cause analysis capabilities.
Customer-oriented approach, attention to detail, and commitment to high-quality support.
Exceptional communication and interpersonal skills.
Ability to work effectively in teams and across organizational boundaries.
Experience owning and coordinating issues across multiple time zones.
Experience identifying operational inefficiencies and implementing automation solutions using AI techniques.

Nice To Haves

Experience with data streaming patterns and tools such as dbt.
Familiarity with CI/CD practices for data pipelines, such as BuildKite or GitHub Actions.
Familiarity with data visualization tools such as Tableau or Looker.
Exposure to database design, performance analysis, and data quality metrics.
Exposure to data science, machine learning, and AI applications.

Responsibilities

Provide on-call technical assistance to analysts, data consumers, and data engineering teams on query optimization, reporting anomalies, complex pipeline issues, failed jobs, and data inconsistencies.
Investigate and resolve data quality issues raised by alerts or stakeholders, following internal and external audit and control practices.
Design and implement batch and real-time data pipelines to a data warehouse or data lake using data transformation technologies.
Enhance monitoring dashboards and support tools.
Lead initiatives to improve data pipelines and data operations capabilities, and mentor and onboard new support data engineers.
Identify operational inefficiencies and implement automation to reduce manual work.
Create and maintain technical support documentation, including FAQs, troubleshooting steps, support runbooks, and a knowledge base of recurring solutions.
Drive operational best practices and shape workflows for consumer support, operations monitoring, incident management, and problem management.
Administer account access to technology-stack tools in accordance with governance policies.
Participate in the team's on-call pager rotation to provide 24/7 coverage and timely responses to critical data issues.