Data Governance Engineer

Your playing field:

• Create and deploy automated checks to proactively identify data quality issues within Tipico's datasets, ensuring alignment with established data governance standards.
• Contribute to the gathering of data quality requirements and maintain technical documentation for these requirements.
• Build, optimize and continually update the data quality platform to ensure its effectiveness in monitoring and enhancing data quality, in accordance with overarching data governance strategies.
• Actively document Tipico's data assets, defining and aligning business and technical terms and concepts to the enterprise data inventory and data catalogue.
• Investigate and analyze the root causes of identified data quality issues to implement solutions and/or fix issues at source systems.
• Work closely with Data Governance Lead to align data quality initiatives with broader governance strategies and policies, fostering a holistic and compliant data ecosystem.
• Generate detailed reports/dashboards on data quality metrics, issues, and resolutions for stakeholders, aiding in informed decision-making and supporting overall data governance objectives.
• Offer expertise on data quality best practices and tools, and conduct training sessions to data engineers, data analysts, development teams, architects, product managers and team leads to evangelise high data quality standards across the organization.

Requirements

Which skills should you bring to the pitch:

• Qualification. Bachelor's Degree (in Computer Science, Information Systems, Statistics, or related field).
• Work Experience. 2+ years of relevant work experience in data engineering, analytics, or a related role.
• Strong analytical and problem-solving skills.
• Excellent in SQL. Including advanced querying, optimisation techniques, and database management.
• Cloud data services expertise. With a solid understanding and practical experience of AWS data technologies, including RedShift, S3, and Athena.
• Real-time data streaming knowledge and familiarity with real-time data streaming technologies like Kafka.
• Data quality frameworks. Strong knowledge and application of data quality frameworks, ensuring the implementation of effective data quality checks and measures.
• Data warehousing concept. The expertise in it plus encompassing data modelling, data transformation and ETL processes.
• In-depth understanding of data lineage, transformation processes, and governance tooling to track and manage the flow of data throughout its lifecycle, ensuring compliance and integrity.
• Tools and Metadata. Proficiency in utilizing data cataloguing tools and practices to organize, categorize, and manage metadata.