Lead Data and AI Integration Specialist
absa
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Job summary
Absa Bank Limited in Johannesburg (Sandton) asks for a Senior/Lead Data and AI Integration Specialist to design, build and evolve backend integrations, data pipelines and observability across Absa's Data and AI estate. You translate platform and cloud data into reusable data products while partnering with architecture, engineering, platform, data, finance and product teams.
The role is 75% hands-on (APIs, SQL, Python) and 25% FinOps-focused reporting, allocation and cost insights for
Qualifications
- Matric (Grade 12) required.
- Diploma or higher in Computer Science, Engineering, Information Systems or related technical discipline.
- Non-technology background is an automatic exclusion for this role.
Responsibilities
- Design, build and maintain resilient APIs, ingestion services and backend data pipelines that integrate cloud billing, Data and AI platform, application, telemetry and enterprise reference data.
- Develop reusable integration components using SQL, Python, REST APIs, JSON, authentication patterns, orchestration and automation.
- Integrate telemetry and metadata from platforms such as AWS, Azure, Databricks, SAS, AI services and internal data platforms.
- Create reliable mappings between technical resources and products, pipelines, models, workloads and owners.
- Improve tagging, metadata quality, lineage and data controls to reduce manual intervention.
- Implement observability, anomaly detection and root‑cause analysis capabilities.
- Collaborate with architecture, engineering, platform, data and product teams to deliver production outcomes.
- Evolve integrations and data products as platforms and priorities change.
Skills
Education
Tools
Job description
With over 100 years of rich history and strongly positioned as a local bank with regional and international expertise, a career with our family offers the opportunity to be part of this exciting growth journey, to reset our future and shape our destiny as a proudly African group.
The Senior/Lead Data and AI Integration Specialist is responsible for designing, building and evolving the backend integration, data engineering, telemetry, metadata and observability capability required across Absa's Data and AI estate. The role translates fragmented platform, cloud and application data into reliable, reusable data products that provide workload‑level visibility across products, pipelines, models, use cases and accountable owners.
Approximately 75% of the role is focused on hands‑on technical engineering and integration, including APIs, SQL and Python development, backend data pipelines, platform telemetry, metadata, lineage, observability, anomaly detection and automation. The specialist must be able to build directly while also partnering with architecture, engineering, platform, data, finance and product teams to deliver scalable outcomes in a complex enterprise environment.
The remaining 25% enables FinOps outcomes through dependable reporting data, allocation and chargeback models, showback, forecasting inputs, unit economics, optimisation insights and savings tracking. Enterprise Cloud FinOps remains accountable for group‑wide cloud standards, commercial levers and consolidated reporting; this role provides the Data and AI workload attribution and technical root‑cause insight needed to make those practices effective.
- Design, build and maintain resilient APIs, ingestion services and backend data pipelines that integrate cloud billing, Data and AI platform, application, telemetry and enterprise reference data.
- Develop reusable integration components using SQL, Python, REST APIs, JSON, authentication patterns, orchestration and automation appropriate to the enterprise technology landscape.
- Integrate telemetry and metadata from platforms such as AWS, Azure, Databricks, SAS, AI services and internal data platforms, subject to the evolving technology estate.
- Create reliable mappings between technical resources and the products, pipelines, models, workloads, use cases, business units and owners that drive consumption.
- Improve tagging, metadata quality, lineage, reconciliation and data controls to reduce manual intervention and strengthen traceability.
- Implement observability, anomaly detection and technical root‑cause analysis capabilities that surface consumption drivers and inefficient engineering patterns.
- Build directly where appropriate and partner with larger architecture, engineering, platform, data and product teams to deliver secure, scalable and supportable production outcomes.
- Continuously evolve integrations, data products and controls as platforms, vendors, use cases and operating priorities change.
- Provide trusted, auditable data products and reporting inputs for Data and AI consumption, cost attribution, forecasting and variance analysis.
- Design and maintain allocation, showback and chargeback logic that links costs to consuming business units, products, pipelines, models and use cases.
- Partner with Finance and Cloud FinOps to align definitions, reporting methodologies, controls and governance while maintaining clear accountability boundaries.
- Develop unit economics and optimisation insights, including relevant cost‑per‑user, cost‑per‑token, cost‑per‑inference, cost‑per‑workload and cost‑per‑use‑case measures.
- Support savings tracking, anomaly investigation and engineering‑led optimisation actions without duplicating enterprise Cloud FinOps commercial or provider‑level responsibilities.
- Leverage and rationalise enterprise tooling, including chargeback, reporting and observability platforms, where appropriate.
- Matric (Grade 12).
- A relevant Diploma or higher qualification in Computer Science, Engineering, Information Systems, Data or a related technical discipline.
- A non-technology academic and career background is an automatic exclusion for this role.
- Bachelor's or Honours degree in a technology, engineering or data-related discipline.
- FinOps certification or demonstrated practical exposure to cloud cost management is advantageous but not essential.
- AWS Certifications (e.g., Cloud Practitioner, Solutions Architect, AWS FinOps or equivalent).
- Microsoft Azure or Google Cloud certifications considered an advantage.
- 7–10 years of relevant experience in backend integration, data engineering, platform engineering, observability or a closely related technical field.
- Demonstrable hands‑on development experience, with strong SQL and Python capability.
- Strong experience integrating systems through REST APIs, JSON, authentication patterns, orchestration and automated data pipelines.
- Experience with cloud and Data and AI platforms such as AWS, Azure, Databricks, SAS, AI services or equivalent enterprise technologies is advantageous.
- Experience with telemetry, metadata, lineage, tagging, observability, anomaly detection and technical root‑cause analysis is advantageous.
- Proven ability to build production solutions directly and to partner effectively with larger architecture, engineering, platform, data and product teams to achieve outcomes.
- Experience creating trusted datasets, data products or reporting layers for cost allocation, chargeback, showback, forecasting, unit economics or optimisation is advantageous.
- Exposure to visualisation and reporting tools such as Power BI, Tableau or equivalent, and to enterprise chargeback tooling such as Magic Orange or Harness, is advantageous.
- Experience working with Finance, Commercial, Reporting or Cloud FinOps teams in a complex enterprise environment is advantageous.
- Banking or financial services experience is not essential; exposure to regulated, complex enterprise environments is preferred.
- Technical Depth – understands backend integration, data engineering and observability patterns and can translate them into production solutions.
- Bias for Delivery – builds, ships and iterates while operating effectively in ambiguity and changing priorities.
- Systems Thinking – connects infrastructure, platforms, workloads, metadata, ownership and financial outcomes end to end.
- Collaboration – contributes hands‑on and mobilises larger technical and business teams to deliver shared outcomes.
- Commercial Acumen – translates technical consumption into useful reporting, allocation and optimisation insight.
- Stakeholder Influence – communicates credibly with technical specialists, Finance, product leaders and executive stakeholders.
- Ownership & Adaptability – takes accountability for outcomes while adjusting designs and priorities as the environment evolves.
- Continuous Learning – remains current on integration, observability, Data and AI platform and FinOps practices.
Performance measures will be agreed and adjusted in line with evolving platform priorities, technology choices and business demand. Outcomes will focus on:
- Reliable, scalable and supportable integrations and backend data products across priority Data and AI platforms.
- Improved telemetry, metadata, lineage, ownership mapping and workload‑level cost attribution.
- Reduced manual reconciliation through automation, data quality controls and reusable engineering patterns.
- Trusted reporting inputs and effective allocation, showback and chargeback modelling.
- Actionable anomaly, unit economics and optimisation insights supported by clear technical root‑cause analysis.
- Effective collaboration with architecture, engineering, platform, product, Finance and Cloud FinOps teams.
- A hybrid working model applies, with the primary base in Johannesburg (Sandton). Candidates based in Cape Town will be considered, subject to periodic travel to Sandton as required.
Bachelor's Degree: Information Technology
Absa Bank Limited is an equal opportunity, affirmative action employer. In compliance with the Employment Equity Act 55 of 1998, preference will be given to suitable candidates from designated groups whose appointments will contribute towards achievement of equitable demographic representation of our workforce profile and add to the diversity of the Bank.
Absa Bank Limited reserves the right not to make an appointment to the post as advertised
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