22 feb
Jr Spain
Estado de México
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.
Required Skills: 5+ years of hands-on experience in data engineering, including Python, Scala , or Java expertise.
Deep understanding of Apache Kafka for stream processing workflows (required) Proficiency in managing and optimizing databases such as PostgreSQL, MySQL, MSSQL , and Oracle .
Familiarity with analytical databases like ClickHouse or similar.
Familiarity with both cloud solutions ( AWS, Azure, Google Cloud ) and on-premises environments as part of cost-optimization efforts.
Knowledge of additional data tools and frameworks such as Redis, RabbitMQ, Superset, Cube.js, Minio, and Grafana (optional but beneficial).
Strong leadership and mentoring skills, with the ability to guide a team and provide technical direction.
Experience ensuring system reliability, scalability, and data integrity through best practices.
Nice to Have: Experience in the iGaming industry Experience with NoSQL databases and stream processing tools like 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.
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