Data Architect Engineer

About the role

To develop and maintain complete data architecture across several application platforms, provide capability across application platforms. To design, build, operationalise, secure and monitor data pipelines and data stores to applicable architecture, solution designs, standards, policies and governance requirements thus making data accessible for the evaluation and optimisation for downstream use case consumption. To execute data engineering duties according to standards, frameworks, and roadmaps Type of Qualification: First Degree Degree in Computer Science, Data Management, or a related field. AWS Certifications (e.g., AWS Certified Data Engineer or Solutions Architect). Experience Required 7-8+ years in Enterprise Data Architecture or Analytics Engineering with a track record of delivered code, not just designs. Expert-level SQL: Deep experience writing complex, performant queries for data transformation and reconciliation. Semantic modelling expertise: Proven experience writing models in dbt, LookML, AtScale, or equivalent tools. Data modelling artefacts: Hands-on production of conceptual, logical, and physical data models. Master Data Management: Experience building matching and survivorship rules for customer data consolidation. Reconciliation framework design: Experience building automated data quality and validation pipelines. AWS Cloud Data stack: Hands-on engineering in S3, Glue, Redshift, and Lake Formation. Financial services background: Strong insurance or financial services industry experience is essential. Stakeholder facilitation: Ability to lead workshops, resolve conflicting definitions, and translate outcomes into code. Regulatory alignment: Working knowledge of POPIA data classification and privacy-by-design. Experience with data observability tooling (Monte Carlo, Soda, Great Expectations) for automated monitoring. Insurance domain certifications Behavioural Competencies: Adopting Practical Approaches Articulating Information Checking Things Developing Expertise Documenting Facts Embracing Change Examining Information Interpreting Data Managing Tasks Producing Output Taking Action Team Working Technical Competencies: Big Data Frameworks and Tools Data Engineering Data Integrity Data Quality IT Knowledge Stakeholder Management (IT)

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