Senior Cloud Data Engineer - KSP

Role overview

What you'll do

• Design and build scalable batch and streaming pipelines for ingestion, transformation, and serving.
• Develop and maintain bronze, silver, and gold data assets with clear ownership, quality, and usability.
• Model bets, offers, rewards, and digital data into reusable datasets for downstream users.
• Implement data transformation logic using tools such as dbt, Spark, and SQL-based frameworks.
• Build and optimize Kafka/Flink or equivalent pipelines for near-real-time data availability.
• Improve data quality and reliability through validation, observability, and robust handling of late or inconsistent data.
• Design data contracts and schemas for reliable integration between event producers and consumers.
• Optimize AWS pipelines and storage for performance and cost efficiency.
• Collaborate with analytics, data science, and machine-learning teams to translate business needs into data assets.
• Contribute to metadata management, lineage, governance, and discoverability.

Platform and data products

Experience and qualifications

• 5+ years of experience in data engineering, building and maintaining scalable data platforms.
• Strong SQL and data modelling skills, including analytical dataset design.
• Experience with modern data stack tools such as dbt, Spark, Airflow, or similar frameworks.
• Experience with cloud data platforms, ideally AWS.
• Understanding of medallion architecture or similar data-layering approaches.
• Experience with streaming technologies such as Kafka, Flink, or similar.
• Strong understanding of data quality, testing, and observability.
• Experience designing schemas and handling data consistency challenges in distributed systems.
• Ability to collaborate with stakeholders to turn business needs into scalable data solutions.
• A proactive approach and ability to operate in evolving environments are expected.

Nice to have

• Sports betting, trading, or financial data experience is a plus.
• Familiarity with event-driven architectures, real-time data products, semantic or metrics layers, data governance tools, or machine-learning and feature-engineering workflows is advantageous.