Data Analytics Engineer focused on data enrichment and governance for a healthcare system—building analytics-ready datasets, supporting Spotfire/Tableau dashboards, and ensuring data quality using SQL, with some ML deployment work.
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