Experience working with a variety of data sources, database types, query authoring (SQL), as well as common data modelling, analytics and visualisation toolsets.
Expert knowledge of working with common Data Warehousing/Data Lake technologies as well as BI tooling (ideally Microsoft Power BI and SQL Server Analysis Services).
Highly proficient in manipulating, processing and extracting value from large, disconnected datasets (near real-time or event-driven data would be an advantage).
Solid understanding of banking/financial services domain (particularly back-office treasury/finance/accounting and regulatory reporting – eg. CBUAE BRF).
Strong analytical skillset and experience working with both structured and unstructured datasets using common tooling (SAS, Python etc).
Experience designing, building, and optimising data pipelines (including CI/CD), ETL/ELTs.
Experience of the full lifecycle of developing and implementing BI/analytics solutions at scale.
Some experience with data orchestration toolsets such as Apache Airflow
Experience working with cloud platforms/services (preferably Azure).
Working knowledge of distributed event stores like Kafka, stream processing, and highly scalable ‘big data’ data stores.
Some knowledge of logging and monitoring tools – ELK, Splunk, Prometheus, Grafana.
Some experience with Docker and Kubernetes would be useful.
Experience fostering stakeholder relationships and working as part of cross-functional teams.
Knowledge of software engineering best practices (full SDLC including code deployment) and how to apply them.
Familiar with agile ways of working (Kanban, Scrum) and tools (Jira)
Identify new/emerging tools, technologies, and methodologies
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