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

Data Analytics Engineer (Data Enrichment & Governance)

Key Responsibilities

• Engage stakeholders to understand data requirements and translate them into analytics‑ready 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 ad‑hoc data requests and exploratory analysis where needed, with emphasis on data preparation over analysis sophistication

Required Skills & Experience

• Strong hands‑on 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 semi‑structured data, including files or message‑based 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 non‑technical stakeholders