We are seeking an experienced Principal Data Engineer to lead a team in developing and maintaining robust, scalable data pipelines, bridging on-premises and cloud environments, and delivering real-time analytics systems. This role requires deep expertise in data engineering and streaming technologies, combined with strong leadership skills to drive the team towards achieving business objectives. The manager will collaborate with cross-functional teams including architecture, product, and software engineering to ensure the delivery of high-quality data solutions aligned with company goals.

Requirements:

• 5+ years of hands-on data engineering experience, including expertise in Python or Scala or Java.
• 3+ years of experience with cloud vendors (AWS, Azure, GCP), data warehouse services (e.g., Redshift, Databricks), and cloud storage (Azure Storage, S3).
• Production experience with KSQL for real-time data analytics and ClickHouse for fast analytics on streaming data.
• Experience with stream processing tools like Apache Kafka, Apache Flink, or Apache Spark Structured Streaming.
• Strong experience with ETL/ELT tools (e.g., Airflow, ADF, Glue, NiFi) and orchestration.
• Proficient in code versioning (Git) and CI/CD pipelines for data projects.
• Experience with data modeling, optimization techniques, and stream processing patterns like windowing and exactly-once processing.
• Experience in managing data warehouses, streaming environments, and scalability in high-volume data settings.
• Excellent leadership and mentorship skills, with at least 1 year in a leadership role.

Nice to Have:

• Experience in iGaming industry
• Experience with NoSQL databases and stream processing tools such as Kafka Connect and Kafka Streams.
• Knowledge of Superset for data visualization.
• Experience integrating real-time machine learning models into streaming environments.
• Expertise with monitoring and observability tools for streaming systems across on-premises and cloud environments.

Responsibilities:

• Lead, mentor, and manage a team of data engineers specializing in streaming technologies.
• Design and maintain scalable data storage solutions with ClickHouse for fast analytics on streaming data.
• Design and implement high-throughput, low-latency streaming data pipelines using Apache Kafka.
• Oversee the development of stream processing applications using Apache Spark or Apache Flink.
• Implement real-time data transformations and analytics using KSQL.
• Ensure integration between on-premises streaming solutions and cloud services (e.g., BigQuery, Looker).
• Lead the design and implementation of ETL processes, extracting, transforming, and loading data into a data warehouse.
• Ensure data integrity, consistency, and accuracy through robust data quality assurance.
• Optimize performance and implement best practices in data engineering, covering data quality, security, and efficiency.
• Collaborate with stakeholders to gather requirements, aligning data strategies with business objectives.
• Stay current with emerging technologies in both streaming and cloud environments, evaluating their potential application.