Senior Analytics Engineer

Accountabilities: You will lead the development and delivery of high-quality data products, ensuring that operational data is transformed into reliable assets that support analytics, reporting, and machine learning initiatives. Design and build data products across the full data lifecycle, from data ingestion and transformation to serving and consumption. Develop robust, scalable, and well-documented data models in collaboration with business users, data engineers, and analytics architects. Extract, transform, and validate data from diverse sources, including JSON and XML feeds, applying effective metadata management practices. Deliver clean, tested, and code-reviewed datasets and data pipelines ready for analytical and machine learning applications. Collaborate with senior stakeholders to define KPI frameworks, reporting standards, and data governance best practices. Work closely with data engineering teams to enhance data product design and integrate datasets for predictive models. Create and maintain dashboards and reporting solutions using Power BI to support data-driven decision-making. Evaluate existing data flows and recommend improvements, influencing platform architects and engineers to optimize data processes. Implement data validation, monitoring, and error-handling mechanisms to ensure data quality, consistency, and accuracy. Maintain comprehensive technical documentation, including data dictionaries and data lineage artefacts. Requirements The ideal candidate combines extensive experience in data engineering and analytics with strong technical expertise, problem-solving abilities, and the capacity to translate complex data into meaningful business insights. At least 8 years of experience in data analytics, data engineering, or a closely related software engineering discipline. Strong SQL skills for data extraction and manipulation, with deep expertise in data modelling and indexing strategies. Hands-on experience with Azure cloud services for data storage and processing, such as Azure Data Lake, Azure Synapse Analytics, or equivalent platforms. Proficiency in Databricks for building data products and performing large-scale data transformations. Experience working with and transforming semi-structured data formats, particularly JSON and XML. Solid understanding of data governance, data quality principles, and security best practices. Strong analytical and problem-solving skills, with the ability to work independently in a fast-paced environment. Excellent communication skills, including the ability to present technical findings and data insights to both technical and non-technical audiences. Master's degree in Computer Science, Data Science, Software Engineering, or a related field, or equivalent professional experience. Nice-to-have qualifications: Experience with additional cloud providers, such as AWS or Google Cloud Platform, alongside Azure. Previous experience in mining, commodities, or heavy industry operational data. Proficiency in additional languages beyond English. Benefits Flexible working arrangements, including remote, office-based, or hybrid options. Competitive salary and comprehensive compensation package. Personalized career development and growth opportunities. Professional development resources, including mentorship programs, technical talks, training sessions, and Centers of Excellence. Active technical communities with regular knowledge-sharing opportunities. Education reimbursement to support continuous learning. Anniversary gifts and recognition. Corporate events and team-building activities. Additional benefits depending on the employee's location.