Data Engineering Lead
Leads OANDA's data engineering team in Kraków, designing and scaling data platforms for its online trading business with hands-on work on Apache Airflow orchestration, dbt modeling, CI/CD pipelines, and rigorous pre-production testing. Hybrid role: 2 days per week in the office.
- Compensation
- Not disclosed
- Location
- Kraków, Poland
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 5+ years of hands-on experience in Data Engineering, Data Platform Administration, or DevOps
- University / Post Graduate Degree (or equivalent working experience)
- 2+ years of proven experience supervising and mentoring technical team members
- Strong proficiency in managing cloud data platforms, Airflow, automated CI/CD pipelines, and multi-tier (dev/stage/prod) environment setups
- Deep understanding of data modeling, scalable architecture design, and infrastructure modernization
- Works independently to configure and optimize IT systems, ensuring smooth integration with existing infrastructure
- Ability to prioritize workloads, manage deployment cycles, and oversee team activities (Agile planning & execution)
- Drives continuous improvement in engineering practices with focus on automation, productivity, and deployment efficiency
- Solves complex, high-quantity technical problems and identifies root causes
- Measures and tracks team performance, fostering a culture of ownership, high-quality delivery, and feedback
- Bridges the gap between technical teams and non-technical stakeholders, communicating clearly and concisely
Full description
Fancy helping to shape the future of FinTech?
We have always been innovators. In 1996 we were the first company to share exchange rate information, free of charge on the internet. Today, we are a world leading online trading group.
Join us to:
- Help build the future of online trading
- Be part of a culture driven by integrity and global impact
- Become part of an award-winning company - check out our full list of awards here (https://www.oanda.com/group/about-us/awards/)
We are only as good as our people. Luckily, our people are the best. Join us!
How do we work?
The Data Engineering Lead is responsible for leading the Data Engineering team to design, build, and scale robust data platforms. Bridging the gap between business needs and technical execution, you will drive end-to-end data solutions from requirements gathering to implementation. A core focus of this role is the hands-on modernization and management of our Data Infrastructure, emphasizing best practices across Airflow orchestration, CI/CD pipelines, and rigorous pre-production testing to ensure the reliable delivery of high-quality data to all stakeholders.
We work in a hybrid model - we'd love to meet you in the office 2 times a week with respect to your own commitments.
In this role, you will:
- Team Leadership & Direction: Lead, mentor, and support a team of data engineers, driving the successful delivery of business-critical data projects.
- Data Infrastructure Management: Oversee the design, architecture, and maintenance of scalable data platform infrastructures and cloud environments.
- Pipeline & Orchestration: Develop and optimize Apache Airflow, to build, monitor, and scale automated, high-performance data pipelines. Utilize dbt as the primary tool for data transformation and modeling across data workflows.
- CI/CD & Preproduction Environments: Implement and enforce rigorous CI/CD practices and comprehensive testing strategies, including unit, integration, and regression tests. Manage robust staging and preproduction environments to guarantee seamless, heavily tested, and reliable deployments to production.
- Architecture & Tooling Selection: Evaluate, select, and install appropriate data tooling based on evolving business needs. Ensure architectures align with industry best practices for data integrity and preservation.
- Operational Excellence: Lead on-call duties and incident management while proactively driving system improvements. Focus on implementing rigorous incident classification, collaborating with upstream teams to drive incident reduction, and leveraging testing and process enhancements to ensure long-term system reliability and stability.
- AI Champion: Act as an AI champion within the team, driving the adoption of AI agents for both software development and incident management, while ensuring that rigorous security and quality standards are maintained throughout the process.