Data Engineer

BALLY’S INTRALOT SA•Toronto, ON

8d•CA$100 - CA$110•Hybrid

About The Position

Bally’s Intralot is a newly formed company established through the combination of Intralot and Bally’s International Interactive, positioning the group as a top iGaming operator and a leading global provider of lottery solutions. Bally’s Intralot is uniquely positioned across digital online gaming markets, lottery, iLottery, and sports betting. Operating at the intersection of technology, regulation, and entertainment. We are optimally positioned to capitalize on strong global growth trends driven by digital adoption and regulatory expansion across lottery and iGaming markets. We’re seeking a passionate Data Engineer who is experienced in data engineering and has a solid understanding of data warehousing, cloud-based solutions, and data pipeline development. You'll be working alongside talented Data Engineers, Data Architects, and Machine Learning Engineers in an agile environment, designing innovative solutions for our Data Platform while supporting and troubleshooting our existing data estate. As a part of our dynamic Data Engineering team, you will help deliver core data components and develop a range of real-time data pipelines. You'll work with large datasets and have the opportunity to apply cutting-edge Big Data technologies and methodologies across our cloud data environments.

Requirements

Strong SQL & Data Warehousing: Ability to design, implement, and optimize data models, and write efficient SQL queries.
Programming Skills: Experience with backend development using Python, and optionally Java.
Data Pipeline Development: Hands-on experience in building data pipelines using open-source frameworks like Spark, DBT and Airflow.
Data Platform Integration: Hands-on experience in integrating Data platforms with different API Architectures (REST, SOAP, Webhook, Websocket).
Cloud Expertise: Experience working with at least one major cloud platform (AWS, GCP, Azure).
Data Lake & Optimization: Knowledge of Parquet, Delta, Spark performance tuning, and optimizing data lakes in Databricks ecosystem.
Data CI/CD deployement: Hands-on experience in building deployment using technologies like Jenkins, GoCD, ArgoCD or Gitlabs.

Nice To Haves

Experience working within the Databricks ecosystem.
Experience in code development in DBT.
Familiarity with TDD (Test-Driven Development) and CI/CD practices.
Experience with streaming technologies such as PySpark, Kafka, Pulsar, or Flink.
Experience working within coding platforms like Cursor, Claude code using AI Agents, MCP and Skills.
Understanding of Kubernetes ecosystem for scaling and container management.
Agile Methodology experience, preferably in a fast-paced environment.
Monitoring & Alerting: Hands-on experience in setting up alerts and monitoring data infrastructure.

Responsibilities

Collaborate with other Data Product Owners, Engineers, and stakeholders to design and implement scalable data solutions.
Develop and optimize data pipelines for data lakehouse and inbound/outbound integrations.
Utilize Python and SQL for data analysis, backend development, and data manipulation.
Apply modern data frameworks such as Spark, DBT and Airflow for building robust data pipelines.
Troubleshoot and resolve issues with our data platform, ensuring high availability and reliability.
Participate in an agile work environment, maintaining open communication and continuous improvement.