Data Engineering, Data Analytics, Data Delivery, Data Governance, Data Migration, Data Quality, Data Lineage, Data Catalogue, SQL, BigQuery, Google Cloud Storage (GCS), Google Cloud Platform (GCP), ETL/ELT, Batch Ingestion, Data Pipelines, Cloud Composer / Airflow, Dataflow or Dataproc, Data Reconciliation, SIT/UAT, Cutover Planning, JIRA / Azure DevOps / Confluence, Power BI / Tableau / Looker
Desired Competencies (Technical/Behavioral Competency)
Must-Have
- 6-12+ years of experience in data, technology delivery, platform integration, governance or migration initiatives, preferably within banking or financial services.
- Hands-on delivery exposure to GCP data platforms, with strong BigQuery experience for SQL-based validation, reconciliation and data investigation.
- Strong working knowledge of Google Cloud Storage (GCS), lake/storage concepts, file-based feeds, ETL/ELT, batch ingestion, transformation, scheduling and monitoring.
- Practical exposure to at least one GCP pipeline or orchestration component, such as Cloud Composer / Airflow, Dataflow or Dataproc / Spark.
- Experience supporting backlog execution, user stories, acceptance criteria, backlog refinement, release planning and dependency tracking.
- Strong understanding of data governance execution, including critical data elements, glossary/data dictionary, metadata, lineage, data quality rules, audit evidence and traceability.
- Technically credible with engineers and architects, with the ability to challenge, clarify, document decisions and translate requirements into delivery-ready stories and controls.
- Experience coordinating SIT/UAT, defect triage, dress rehearsals, cutover runbooks, hypercare and post-release verification.
- Basic understanding of cloud environments, access/IAM concepts and operational readiness is required.
Good-to-Have
- Exposure to the Hadoop ecosystem, including Hive, HDFS, Spark, YARN, Impala and related delivery considerations.
- Familiarity with Microsoft Fabric concepts and components, such as OneLake, pipelines, Lakehouse/Warehouse, semantic models and governance integration.
- Exposure to data governance or catalogue tools such as Collibra, Alation or Purview; Python for lightweight data checks or automation; and BI tools such as Power BI, Tableau or Looker.
- Awareness of Hong Kong regulatory expectations and PDPO considerations is an advantage.
Role descriptions / Expectations from the Role
- Support the Senior Technical Product Owner / Data Delivery Lead in delivering Hong Kong market-related data changes across backlog execution, governance controls, migration readiness, testing, cutover and stakeholder cadence.
- Translate business and data requirements into delivery-ready user stories, acceptance criteria, decision logs, controls and traceable implementation actions.
- Coordinate data governance deliverables, including critical data elements, glossary/data dictionary, metadata, lineage, data quality rules, audit evidence and traceability.
- Support migration and reconciliation through source-to-target mapping, profiling, validation rules, reconciliation controls, readiness dashboards and sign-off packs.
- Organise SIT/UAT, defect triage, dress rehearsals, cutover runbooks, hypercare and post-release verification while coordinating working-level stakeholders across Business, Operations, IT, Risk/Compliance and vendors.