Data Engineer

• Design, build, and deploy large-scale distributed systems and high-throughput data pipelines using Go and cloud-native technologies.
• Lead system-wide architectural decisions, focusing on data flow, performance, and resilience.
• Actively contribute to the codebase with high quality code.
• Lead major technical initiatives, reduce technical debt and ensure the platform meets the reliability and scalability SLAs. Champion best engineering practices, code quality, testing and maintainability.
• Collaborate with product and engineering teams and R&D management to define the technical roadmap, review architecture and mentor junior engineers.

Principal Data Engineer

• Design, build, and maintain data ingestion and transformation pipelines using Databricks
• Develop and support data pipelines across Bronze, Silver and Gold layers
• Implement batch and streaming data pipelines using tools such as Databricks, Spark, and Azure Data Factory (ADF)
• Work closely with Data Architects and business stakeholders to understand requirements
• Support the implementation of data quality checks, validation rules, and monitoring processes
• Assist in optimising data pipeline performance, scalability, and cost efficiency
• Follow established best practices for data governance, lineage, and metadata management
• Support testing, deployment, and release processes
• Create and maintain technical documentation
• Stay up to date with Databricks features and modern data engineering practices

Senior Databricks Data Engineer

• Analyze user behavior, product performance, and key business metrics to identify trends and opportunities.
• Create and maintain interactive dashboards and reports using tools like Tableau, Looker, Power BI, or Google Data Studio.
• Collect, clean, and structure large datasets to ensure accuracy and usability.
• Define and track KPIs for marketing, product, and business performance.
• Work with SQL, Python, or R to extract, analyze, and manipulate data.
• Collaborate with product managers, engineers, and marketing teams to provide data-driven insights.
• Conduct A/B testing and experiment analysis to improve product features and user engagement.
• Identify bottlenecks and opportunities in user journeys and customer funnels.
• Present findings in a clear and compelling way to stakeholders and leadership teams.
• Stay up to date with industry trends, analytics best practices, and emerging data tools.

• Design, build, and maintain scalable data pipelines (ETL/ELT) to support analytics and AI use cases
• Develop and optimise data architectures, data lakes, and data warehouses
• Work closely with stakeholders to translate business needs into data solutions and insights
• Build and maintain data processing scripts and automation tools (Python, SQL)
• Support and enhance AI/analytics platforms and data products
• Implement monitoring, testing, and performance tuning of pipelines
• Collaborate with cross-functional teams (data scientists, engineers, product teams)

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Principal Data Engineer

Senior Databricks Data Engineer

Data Engineer

Senior Data Engineer