Data Engineer

FanDuel is hiring a Data Support Engineer to maintain the reliability and performance of its data infrastructure and pipelines. The role supports data consumers through technical troubleshooting, proactive monitoring, operational improvements, and reliable delivery of data.

Responsibilities

• Provide on-call technical assistance to analysts, data consumers, and data engineering teams, responding to inquiries relating to query optimization, reporting anomalies, complex data pipeline issues, failed jobs, and data inconsistencies
• Investigate and resolve data quality issues in response to system alerts or notifications from stakeholders, ensuring compliance with internal/external audits and control practices
• Design and implement batch and real-time data pipelines to the data warehouse or data lake using data transformation technologies, enhancing monitoring dashboards and support tools
• Lead impactful initiatives to enhance data pipelines and other data operations capabilities while mentoring and onboarding new support data engineers. Identify operational inefficiencies and implement automation solutions to reduce manual intervention
• Create and maintain technical support documentation, including FAQs, troubleshooting steps, support runbooks, and a knowledge base of solutions to recurring issues
• Drive operational best practices and shape workflows for routine consumer support, operations monitoring, incident management, and problem management
• Administer account access to tools across the technology stack in accordance with our governance policies
• Able to join team's On-call Pager duty rotation to ensure 24/7 coverage and timely response to critical data issues

Preferred

• Experience with data streaming patterns and tools like Dbt
• Familiarity with CI/CD practices for data pipelines (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
• Data streaming patterns
• dbt
• CI/CD practices
• Data pipeline development
• Buildkite
• GitHub Actions
• Data visualization
• Tableau
• Looker
• Database design
• Performance analysis
• Data quality metrics
• Data science
• Machine learning
• AI applications

Benefits

• Health plans to choose from (some as low as $0 per paycheck), including programs for fertility and family planning, mental health support, and fitness benefits (benefits differ across location, role, and level).
• Generous paid time off (PTO & sick leave) (benefits differ across location, role, and level).
• Annual bonus and long-term incentive opportunities based on performance (benefits differ across location, role, and level).
• 401(k) with up to a 5% match (benefits differ across location, role, and level).
• Commuter benefits (benefits differ across location, role, and level).
• Pet insurance (benefits differ across location, role, and level).
• Medical, vision, and dental insurance.
• Life insurance.
• Disability insurance.
• Short-term or long-term incentive compensation, potentially including cash bonuses and stock program participation.
• Paid personal time off and 14 paid company holidays.
• Paid sick time in accordance with applicable state and federal laws.
• Hybrid work arrangement.

Company

• Sports betting, daily fantasy sports, and casino gaming platform.
• New York, New York, USA
• http://www.fanduel.com/
• Founded in 2007
• 1001-5000 employees

Qualifications

• Minimum of 3+ years of experience in data engineering with focus on operational support
• Advanced SQL skills with proven query optimization experience
• Strong problem-solving skills with ability to take ownership and accountability for issues through resolution
• Python programming proficiency for automation and data processing
• Experience with cloud data platforms (AWS, Databricks, Spark)
• Understanding of data warehousing and ETL/ELT processing
• Experience with dimensional data modeling, batch data ingestion, and real-time technologies like Kafka or Kinesis
• Working knowledge of orchestration and monitoring tools (Airflow, Datadog, Databand, DataFold, Monte Carlo, Prometheus, Grafana)
• Strong incident management and root cause analysis capabilities
• Customer-oriented mindset with focus on delivering high-quality support services and attention to detail
• Exceptional communication and interpersonal skills
• Proven ability to work effectively within 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
• Experience with data streaming patterns and tools like Dbt
• Familiarity with CI/CD practices for data pipelines (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

Required skills

• AWS, Amazon Kinesis, Apache Airflow, Apache Kafka, Apache Spark, Batch Data Ingestion, Data Warehousing, Databricks, Datadog, Dimensional Data Modeling, ETL/ELT, Incident Management, Python, SQL

Preferred skills

• Experience with data streaming patterns and tools like Dbt

Experience

• 3+ years