Senior Data Engineer

Gametime is seeking a Senior Data Engineer to help shape the future of our data platform. In this high-impact role, you’ll lead the design, implementation, and evolution of core data systems that power decision-making across analytics, product, marketing, and operations.

You’ll own critical infrastructure across both batch and streaming pipelines, contribute to architectural strategy, and partner with stakeholders across the business to build scalable, reliable data solutions. As a senior member of the team, you’ll provide technical leadership, drive best practices, and mentor others as we continue to scale.

We’re operating on a modern, event-driven stack, and you’ll play a key role in growing and hardening our capabilities in areas like observability, real-time processing, and platform reliability.

Our stack includes: Snowflake · Airflow · dbt · Kafka (Confluent) · AWS (S3, Lambda, ECS, EKS) · Terraform · Python · SQL · Fivetran · Sigma · Monte Carlo

Key Responsibilities

• Architect and Own Core Data Infrastructure: Design and lead implementation of scalable, resilient batch and streaming pipelines using Airflow, Python, and Kafka.
• Lead Platform Evolution: Shape the roadmap and technical direction of the Data Platform, advocating for best practices in architecture, observability, cost efficiency, and quality.
• Model & Transform Data: Own development of modular, production-grade dbt models that surface business logic for downstream analytics and data science.
• Infrastructure as Code: Manage deployment of cloud infrastructure via Terraform across AWS environments.
• Drive Quality and Monitoring: Establish and improve systems for data validation, pipeline reliability, and automated alerting.
• Mentor & Collaborate: Provide technical leadership through code reviews, architecture discussions, and mentorship across the data and engineering orgs.
• Cross-Team Partnerships: Work closely with stakeholders in Product, Analytics, and Engineering to scope and deliver impactful data initiatives.
  
  

Minimum Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent experience.
• 5+ years of experience in data engineering, software engineering, or platform development.
• Strong fluency in SQL and Python for both pipeline development and data analysis.
• Deep experience with modern data warehouses (e.g., Snowflake) and cloud infrastructure (AWS preferred).
• Expertise with workflow orchestration tools like Airflow and data transformation tools like dbt.
• Solid understanding of data modeling, software design principles, and engineering best practices.

Preferred Qualifications

• Experience with event-driven architectures and real-time data processing via Kafka or similar.
• Hands-on experience managing infrastructure with Terraform or other Infrastructure-as-Code (IaC) tools.
• Proven track record of leading complex technical initiatives and mentoring other engineers.
• Familiarity with data governance, lineage, or observability tooling.

What success looks like:

• Lead and deliver major platform initiatives that improve scalability, reliability, or usability of the data stack.
• Reduce platform maintenance by automating operational workflows and improving observability.
• Drive architectural improvements that reduce query cost, improve performance, or unblock new use cases.
• Influence engineering culture by mentoring teammates, improving documentation, and driving technical standards.