Senior Executive/Assistant Manager (Data Analytics Engineer), AIO Innovation Office (Contract)
Senior Executive/Assistant Manager (Data Analytics Engineer), AIO Innovation Office (Contract)
National University Health SystemFresher
- Posted 2 months ago
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Job Description
Data Analytics Engineer (Data Enrichment & Governance)
Key Responsibilities
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
- 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
More Info
Key Skills
About Company
Skills:
Sql, Tensorflow, Deep Learning, Nlp, MLops, Pytorch, Gcp, Computer Vision, Hl7, Azure, Dicom, Python, AWS, Scikit-learn, FHIR, R, time-series analysis