Data DevOps Engineer

PTT Consulting specializes in agile IT staffing solutions, providing flexible and scalable services through a body leasing model. Since 2015, they have been connecting businesses with top IT talent across multiple specializations.

The client is a global leader focused on lottery solutions, delivering safe and secure technology with a strong commitment to customers as a dedicated lottery service provider. They leverage 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.

• Background in computer science, engineering, physics, mathematics, or equivalent.
• Strong background in system administration and shell scripting.
• Strong hands-on experience with Python and SQL.
• Strong problem-solving skills.
• Eagerness to learn new approaches and technologies.

Nice to have:

• Experience with Azure cloud services.
• Experience with Databricks and Spark.
• Experience with administration of large clusters.
• Experience with Private endpoint configuration.
• Understanding of modern Big Data technologies (Hadoop, Spark, Kafka, NoSQL).
• Experience with virtualization.
• Experience with containerization (Docker, Kubernetes).
• Experience with CI/CD tools.
• Experience with Infrastructure as code.
• Experience with configuration or infrastructure management tools.

• Communicate effectively with technical and non-technical stakeholders.
• Support and host discussions within a multidisciplinary team, with potentially difficult dynamics.
• Be an advocate for the team externally, and manage differing perspectives.
• Understand and help teams apply a range of techniques for data profiling.
• Perform source system analysis from different sources.
• Bring multiple data sources together in a conformed model for analysis.
• Establish enterprise-scale data integration procedures across the data development life cycle and ensure adherence.
• Manage resources to ensure data services work effectively at an enterprise level.
• Identify areas of innovation in data tools and techniques and recognize appropriate timing for adoption.
• Establish standards, keep them up to date, and ensure adherence.
• Keep abreast of best practices in the industry.
• Understand concepts and principles of data modelling and produce relevant data models.
• Recognize opportunities for reuse and alignment of data models in different organizations.
• Design methods to categorize data models within an organization.
• Design appropriate metadata repositories and present changes to existing ones.
• Understand a range of tools for storing and working with metadata.
• Provide oversight and advice to less experienced team members.
• Ensure appropriate actions are taken to resolve problems as they occur.
• Coordinate teams to resolve problems and implement solutions and preventative measures.
• Use agreed standards and tools to design, code, test, correct, and document moderate-to-complex programs and scripts from agreed specifications and iterations.
• Collaborate with others to review specifications where appropriate.
• Demonstrate thorough understanding of technical concepts required for the role and explain their fit in the wider technical landscape.
• Review requirements and specifications and define test conditions.
• Identify issues and risks associated with work.
• Analyze and report test activities and results.