Data Engineer

Working hours: 12:00 PM – 8:00 PM (CEST)

Requirements

• Bachelor’s degree in computer science, Engineering, Information Systems, or related field.

• Proven experience in data engineering, software development, or related roles.

• Proficiency in programming languages is commonly used in data engineering (e.g., Python, Java, Scala, etc.).

• Strong knowledge of database systems, data modeling techniques, and SQL proficiency.

• Proficiency with ETL tools commonly used in data engineering (e.g., Databricks, Azure Data Factory).

• Experience with big data technologies and frameworks (e.g., Hadoop, Spark, Kafka, etc.).

• Familiarity with cloud platforms and services (e.g., AWS, Azure, GCP, Snowflake, etc.).

• Excellent problem-solving skills and attention to detail.

• Experience with Cloudability Data Platform a plus

• 6-8 years’ experience

Responsibilities

• Build a FinOps Data Platform – design, implement, and optimize end-to-end data pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data to produce a cloud cost chargeback dataset.

• Develop robust ETL (Extract, Transform, Load) processes to integrate data from diverse sources into our data ecosystem.

• Implement data validation and quality checks to ensure accuracy and consistency.

• Design and maintain data models, schemas, and database structures to support analytical and operational use cases.

• Optimize data storage and retrieval mechanisms for performance and scalability.

• Evaluate and implement data storage solutions, including relational databases, NoSQL databases, data lakes, and cloud storage services.

• Build and maintain integrations with internal and external data sources and APIs.

• Implement APIs and web services for data access and consumption.

• Ensure compatibility and interoperability between different systems and platforms.

• Ability to build reliable, observable data workflows.

• Knowledge with cloud platform (Azure data factory, Synapse, AWS S3, BiqQuery, etc.)

• Scheduling, dependencies, retries, backfills and data validation.

• Configure and manage data infrastructure components, including databases, data warehouses, data lakes, and distributed computing frameworks.

• Monitor system performance, troubleshoot issues, and implement optimizations to enhance reliability and efficiency.

• Implement data security controls and access management policies to protect sensitive information.

• Collaborate with data analytics, and other stakeholders to understand data requirements and deliver tailored solutions.

• Document technical designs, workflows, and best practices to facilitate knowledge sharing and maintain system documentation.

• Provide technical guidance and support to team members and stakeholders as needed.

Client

A global leader with a sharp focus on lottery solutions. A confident step forward building on a long history of delivering safe and secure technology, demonstrating strong commitment to customers as a dedicated lottery service provider. Leveraging collective insight, experience, and expertise to create reliable and engaging solutions that help lottery clients achieve objectives, meet player needs, and deliver meaningful benefits to communities.