Director of Data Engineering

Are you ready to build the data backbone behind AI-powered sports?

Pixellot, the global leader in AI-based automated sports production, is transforming how games are captured, produced, and shared. We’re looking for a hands-on leader who thrives on both building and leading — someone who can architect, code, and guide the data domain to scale. Our systems power professional-grade broadcasting for leagues, clubs, and communities worldwide. To accelerate our next wave of AI and product innovation, we’re hiring a hands-on Director of Data Engineering to architect, lead, and scale Pixellot’s data platform.

In this role, you’ll set the data strategy and get your hands dirty building it, creating the foundations that enable world-class analytics, machine learning, and GenAI applications across video, product, and business domains. You’ll partner closely with Data Science/ML, Product, and Engineering. Early on, you’ll roll up your sleeves to assess our current data landscape, define the target architecture, implement core pipelines and standards, and build your team from the ground up, creating the structure and culture for success.

If you’ve turned fragmented, messy data into a high-reliability, high-velocity data and ML platform—and you’re excited to do it again in the world of sports and video—this is the place for you.

As part of your role, you will:

• Be a hands-on technical leader, building the first core components of the data platform while shaping the long-term strategy
• Lead our data technology strategy, defining the vision, architecture, and operating model for Pixellot’s data platform—batch, streaming, and real-time—aligned with company goals
• Lead and grow a department of data engineers, setting direction and ensuring delivery at scale
• Audit existing data sources and flows, designing a unified event taxonomy, schemas, and contracts across products and services, while establishing standards for naming, lineage, governance, and documentation
• Design and implement ingestion, transformation, and serving layers (lake/lakehouse, warehouse, feature store), setting up orchestration, CI/CD, observability, and cost governance
• Partner with Data Science/ML to deliver reliable training/validation datasets, model-ready features, and automated re-training workflows for video and non-video signals
• Introduce data validation, testing, and monitoring (schema enforcement, SLAs/SLOs, anomaly detection, lineage) to ensure trust in downstream analytics and models
• Research and implement emerging AI/GenAI tools and technologies, applying an MVP/PoC-driven approach to accelerate innovation
• Work with Video, Cloud, and Product teams to integrate camera/sensor and app data, aligning metrics, experimentation, and privacy/security requirements
• Build your team from the ground up, creating the structure and culture for success
• Be a great listener who thrives on understanding team and business needs, fostering collaboration and clarity across domains
  
  

• Extensive experience leading end-to-end data engineering initiatives in cloud environments
• Proven track record transforming fragmented data ecosystems into scalable, reliable platforms supporting analytics and ML
• Strong hands-on skills in Python, SQL, and distributed processing (Spark or equivalent)
• Expertise with modern data stacks: orchestration (Airflow or similar), transformation (dbt or equivalent), data warehouses (BigQuery/Snowflake/Redshift), lake/lakehouse (e.g., S3/GCS + table formats), and CI/CD for data
• Experience designing event schemas, data contracts, and metadata/lineage frameworks; implementing data quality controls and cost optimization
• Demonstrated partnership with Data Science/ML teams to deliver training datasets, features, and production-grade data services
• Experience with AI/GenAI tools and ecosystems, with the ability to evaluate, implement, and operationalize them for business and product use cases
• Excellent leadership, stakeholder management, and communication skills in a global setting. Fluent English
  
  

• Background with video/sensor data, computer vision pipelines, or sports tech
• Experience with streaming (Kafka/Pub/Sub), feature stores, and MLOps (model registries, experiment tracking, re-training)
• Familiarity with BI tooling (Looker/Tableau) and self-serve analytics enablement
• Multi-cloud (AWS & GCP) and Kubernetes/Docker exposure
  
  

• Shape the data foundation that powers the global leader in AI-driven sports broadcasting
• Lead groundbreaking initiatives — from reimagining architecture to deploying real-world systems used by teams and leagues worldwide
• Turn complex data and video into unforgettable sports moments, making every fan experience smarter and more engaging
• Work with passionate innovators in a collaborative culture that values creativity, ownership, and impact