Data Engineer

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

Project / Role

• 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

Requirements

• 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)

What do we offer?

• We focus on long-term relationships based on fair principles and reliability

• Co-financing of the Multisport card and Medicover private healthcare

• Modern office available

• Team bonding activities, internal courses, conferences, certifications