Senior Data Engineer - FanDuel, Hybrid

Senior Data Engineer - FanDuel, Hybrid

Senior Data Engineer 1

About Betfair Romania Development​:

Betfair Romania Development is the largest technology hub of Flutter Entertainment, with over 2,000 people powering the world’s leading sports betting and iGaming brands. Exciting, immersive and safe experiences are delivered to over 18 million customers worldwide, from our office in Cluj-Napoca. Driven by relentless innovation and commitment to excellence, we operate our own unbeatable portfolio of diverse proprietary brands such as FanDuel, PokerStars, SportsBet, Betfair, Paddy Power, or Sky Betting & Gaming.

Our Values:

The values we share at Betfair Romania Development define what makes us unique as a team. They empower us by giving meaning to our contributions, and they ensure that we consistently strive for excellence in everything we do. We are looking for passionate individuals who align with our values and are committed to making a difference.

Win together | Raise the bar | Got your back | Own it | Positive impact

About the Brand: FanDuel

FanDuel is a leading force in the sports-tech entertainment industry, redefining how fans engage with their favourite sports, teams, and leagues. As the premier gaming destination in North America, FanDuel operates across multiple verticals, including sports betting, daily fantasy sports, online gaming, advance-deposit wagering, and media.

Exposure to emerging innovative technologies in business applications is an advantage

The Role

We are looking for a Senior Data Engineer to design and build scalable, reliable, and high-quality data solutions that power analytics, machine learning, and business operations. You’ll be a hands-on contributor who plays a key role in shaping the technical foundation of our data ecosystem.

As a senior member of the team, you’ll work closely with engineers, analysts, product managers, and data scientists to deliver data pipelines and platforms that drive meaningful business outcomes. The ideal candidate is a strong problem solver, a team player, and someone who brings both technical depth and a collaborative mindset to every challenge.

If you’re excited by this challenge and want to work within a dynamic company, then we’d love to hear from you.

Key Accountabilities & Responsibilities:

Build & Maintain Data Infrastructure

• Design, develop, and maintain scalable ETL/ELT pipelines to support data products, analytics, and operational systems.

• Work with large-scale, complex datasets and ensure data is clean, well-modeled, and accessible across the organization.

• Develop and maintain batch and streaming data pipelines and data platforms using modern tools and technologies.

Deliver High-Quality, Reusable Solutions

• Build modular, reusable data models and assets using tools like Spark, dbt or similar frameworks.

• Collaborate with stakeholders to understand business needs and translate them into reliable, production-ready data solutions.

• Ensure data quality, accuracy, and consistency through validation frameworks and monitoring.

Contribute to Engineering Excellence

• Follow and promote software engineering best practices, including testing, version control, documentation, and code reviews.

• Actively participate in technical design discussions, and architecture reviews.

• Help evolve our data platform and tooling to improve performance, developer experience, and scalability.

Collaborate & Communicate

• Partner with data scientists, analysts, and product managers to support data-driven decision-making.

• Clearly communicate technical concepts, project status, and risks to stakeholders.

• Share knowledge and mentor junior engineers through code reviews and collaborative problem-solving.

Skills, Capabilities & Experience Required:

• 5+ years of experience in data engineering or software engineering with a focus on building data pipelines and platforms.

• Strong SQL skills and fluency in at least one programming language (e.g., Python, Scala, or Java).

• Hands-on experience with modern data tools such as Airflow, Spark, dbt, Kafka, or Databricks.

• Deep understanding of data modeling, data warehousing, and best practices for data quality and observability.

• Experience with cloud-based data platforms (e.g., AWS, GCP, Azure).

• Strong problem-solving skills and ability to work independently in a fast-paced environment.

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