Data Engineer

As a Data Engineer, you will play a key role in building and maintaining the data infrastructure that powers machine learning, analytics, and business initiatives. You will design scalable batch and near-real-time pipelines capable of handling millions of data changes every day. Your work will ensure data remains accurate, consistent, reliable, and readily available across the organization. You will collaborate closely with data scientists, MLOps engineers, product owners, and BI analysts to translate business needs into robust data solutions. The role combines hands-on engineering with opportunities to improve processes, architectures, integrations, and infrastructure scalability. You will work in a remote-first environment with flexible working hours and a strong focus on autonomy and collaboration. This is an opportunity to contribute to a rapidly growing, data-intensive environment while working with modern cloud and data technologies. Accountabilities

• Data pipeline development: Design, develop, test, optimize, and maintain scalable batch ETL and near-real-time data pipelines capable of processing high-volume data sources.
• Data architecture: Build and evolve reliable data architectures that support machine learning, data science, business intelligence, and operational requirements.
• Data quality: Ensure data is accurate, consistent, reliable, and fit for downstream analytical and operational use.
• Scalability and optimization: Identify opportunities to improve internal processes, optimize data delivery, and redesign infrastructure to support increasing scale and complexity.
• API integrations: Develop and maintain new API integrations to accommodate growing data volumes and evolving business requirements.
• Cross-functional collaboration: Work closely with data scientists, MLOps engineers, product owners, and BI analysts to understand business processes, system architecture, and specific product needs.
• Data infrastructure: Contribute to the development and maintenance of data platforms, databases, integrations, and cloud infrastructure supporting production workloads.

• Education: Bachelor’s degree or equivalent practical experience in Computer Science, Engineering, Mathematics, or a related technical discipline.
• Professional experience: 3+ years of experience in data engineering, data platforms, business intelligence, or a related field.
• Data-centric applications: Proven experience implementing data warehouses, operational data stores, data integration solutions, or similar data-focused applications.
• Database expertise: Experience working with large-scale production relational and NoSQL databases.
• Data modeling: Strong understanding and practical experience with data modeling principles.
• Architecture knowledge: General understanding of modern data architectures and event-driven architectures.
• SQL: Strong proficiency in SQL for querying, transforming, and analyzing data.
• Programming: Familiarity with at least one scripting language, preferably Python.
• Data technologies: Hands-on experience with Apache Airflow and Apache Spark.
• Cloud platforms: Solid understanding of AWS data services, including S3, Athena, EC2, Redshift, EMR, EKS, RDS, and Lambda.
• Machine learning: Understanding of machine learning models is an advantage.
• Containerization: Familiarity with Docker, Kubernetes, or similar containerization and orchestration technologies is beneficial.
• Industry knowledge: Experience or knowledge of the gaming industry is a plus.
• Collaboration: Strong communication skills and the ability to work effectively with technical and business stakeholders across multiple disciplines.

• Remote-first environment: Work remotely with a strong focus on flexibility, autonomy, and sustainable working practices.
• Competitive compensation: Competitive salary with individual performance-based bonuses paid quarterly.
• Paid leave: 28 days of paid annual leave.
• Flexible working hours: Core working hours from 10:00 AM to 3:00 PM in your local time zone, with flexibility outside these hours.
• Additional bonuses: Opportunities to earn referral bonuses and flash bonuses.
• Quality equipment: Access to high-quality, professional equipment to support effective remote work.
• Annual retreats: Company retreats designed to encourage collaboration, networking, and stronger connections across the team.
• Professional growth: Exposure to large-scale data engineering challenges, modern cloud technologies, and cross-functional initiatives.