Data Engineer

At Pretius, we are looking for Data Engineer to a project for global-scale platform in the field of gaming and lotteries.

• 8+ years of experience in data engineering, analytics engineering, or similar data-focused roles
• Expert-level proficiency in Python for data processing, pipeline development, and automation
• Advanced SQL skills, including query optimization and complex analytical transformations
• Strong experience with relational and analytical databases (e.g., PostgreSQL, Snowflake, BigQuery, Redshift, Synapse)
• Hands-on experience designing and implementing data warehouse architectures (ETL/ELT, batch, near-real-time)
• Proven experience with big data processing frameworks such as Apache Spark (PySpark, Spark SQL)
• Strong cloud experience across AWS, Azure, and/or GCP, including core data services
• Experience building and operating scalable data pipelines using orchestration tools (Airflow, ADF, Prefect, Dagster)
• Understanding of distributed systems principles and large-scale data processing challenges
• Strong knowledge of data quality, governance, security, and compliance best practices
• Experience with DevOps practices, including CI/CD, Git, and Infrastructure as Code (Terraform or equivalent)
• Ability to design scalable, production-grade data solutions in complex enterprise environments

Nice to have:

• Familiarity with streaming technologies (Kafka, Kinesis, Pub/Sub)
• Experience with dbt and BI tools (Power BI, Tableau, Looker)

• Design, build, and maintain scalable, production-grade data pipelines using Python (ETL/ELT) and orchestration tools
• Write and optimize advanced SQL queries for efficient data extraction, transformation, and performance tuning
• Design and implement scalable data models (star/snowflake schema) for analytics and reporting
• Build and maintain end-to-end data warehouse solutions, including batch and near-real-time ingestion, data marts, and semantic layers
• Work with Apache Spark (PySpark, Spark SQL) for large-scale data processing and analytics
• Develop and operate cloud data solutions across AWS, Azure, and/or GCP (e.g., S3, Glue, EMR, Redshift, ADLS, Data Factory, Synapse, BigQuery)
• Design scalable, secure, and cost-efficient data architectures with FinOps awareness
• Build and maintain reliable data pipelines using orchestration tools (Airflow, ADF, Prefect, Dagster) with proper scheduling, retries, and monitoring
• Ensure data reliability through validation, monitoring, idempotent design, and failure recovery mechanisms
• Develop streaming and real-time data pipelines using Kafka, Kinesis, Pub/Sub, or Event Hubs where required
• Implement data quality, governance, and security standards (PII protection, encryption, RBAC, data lineage)
• Apply DevOps practices including Git, CI/CD, Infrastructure as Code, and production monitoring
• Integrate external APIs and SaaS data sources into data platforms