Senior Executive/Assistant Manager, AIO Innovation Office (1 years contract)

Data Analytics Engineer (Data Enrichment & Quality Focus)

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


• 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