Senior Executive/Assistant Manager (Data Analytics Engineer), AIO Innovation Office (Contract)

Data Analytics Engineer (Data Enrichment & Governance) Key Responsibilities Engage stakeholders to understand data requirements and translate them into analyticsready datasets, with an initial focus on supporting dashboard delivery Ingest, preprocess, and transform data from enterprise systems and external feeds (e.g. files, messages) into structured tables and views using SQL and BI/analytics tools Enrich and extend EAI data coverage, including working with service and platform teams to extract additional fields from source systems and improve data completeness Build and support dashboards and visualisations (e.g. Spotfire, Tableau) primarily by ensuring data accuracy, consistency, and suitability for reuse Perform data validation, reconciliation, and quality checks to improve reliability of downstream dashboards and analytics Support platform and analytics migrations (e.g. Healix), including data validation, pipeline adjustments, and dashboard rebuilds Maintain and improve data documentation, data dictionaries, definitions, and mappings to support governance, quality improvement, and stakeholder confidence Work closely with data engineers, service teams, and analysts to operationalise data pipelines and datasets, rather than focusing on visual design alone Support adhoc data requests and exploratory analysis where needed, with emphasis on data preparation over analysis sophistication

Required Skills & Experience Strong handson experience with SQL for data ingestion, preprocessing, transformation, and view creation Experience using BI / analytics tools (e.g. Spotfire, Tableau, Databricks SQL) as part of data preparation and dashboard support Experience working with structured and semistructured data, including files or messagebased inputs Familiarity with data quality management, data definitions, and governed data environments Ability to understand and document data semantics clearly, and maintain data knowledge for reuse Experience with end-to-end ML lifecycle and deploying ML models using tools such as Docker, Kubernetes, MLflow, SageMaker, Azure ML or equivalent platforms Comfortable working across multiple workstreams involving data enrichment, remediation, and migration support Able to communicate data issues, constraints, and definitions clearly to technical and nontechnical stakeholders