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JD — Data Architect

Owner Classification Review Date Status
People Operations Internal April 2027 Active

Job Description: Data Architect

Department: Technology & Digital — Data
Reports to: Head of Data

Role Overview

Simpaisa's data infrastructure is today a collection of transactional databases. The business is preparing to build a Data Engineering function that will transform that into an analytics and AI/ML platform — CCQ dashboards, reconciliation automation, corridor optimisation, and eventually real-time decisioning.

The Data Architect will design the data infrastructure that makes this possible. This means: deciding how transaction events flow from operational systems into analytical stores, defining the data models that power the CCQ framework, designing the event schema that will underpin the Phase 2 event sourcing migration, and ensuring data quality, security, and regulatory compliance across every data pipeline.

This is a design-first role. You will work closely with Data Engineers (who build the pipelines), Data Scientists (who consume the data), and Solution Architects (who design the operational systems that generate the data). The quality of your architecture determines whether all of them can do their jobs.

Key Responsibilities

  • Design and own Simpaisa's data architecture blueprint — data models, storage solutions, pipeline patterns, and standards for data quality, security, and governance.

  • Design the analytical data platform: data warehouse or data lake architecture, including ingestion from transactional systems (MySQL/PostgreSQL), transformation layer (dbt or similar), and consumption layer (dashboards, ML features).

  • Design the event schema for the Transaction Lifecycle Architecture (Phase 2 event sourcing migration) — define the event types, payloads, and projection patterns that will replace direct state queries.

  • Define data models for the CCQ framework — Coverage, Cost, Quality metrics per corridor and operator. This is the analytical foundation for CSNO's network management.

  • Evaluate and recommend data platform tooling: data warehouse (Snowflake, BigQuery, Redshift), streaming (Kafka, Kinesis), orchestration (Airflow, Dagster), and transformation frameworks.

  • Ensure data architecture meets DFSA, SBP, and other regulatory data residency and retention requirements.

  • Collaborate with Security to ensure data classification, encryption, and access control are embedded in every pipeline design.

  • Document data architecture designs, data flows, and data models clearly enough that engineers can implement them without ambiguity.

  • Define and enforce data quality standards across all pipelines.

Required Skills and Experience

  • Data modelling: Deep expertise in relational and dimensional modelling. Ability to design data models for both OLTP (operational) and OLAP (analytical) use cases.

  • Data pipelines: Experience designing ETL/ELT pipelines at scale — batch and streaming. Understanding of CDC (change data capture) patterns for extracting from operational databases.

  • Event architecture: Familiarity with event sourcing and event-driven architectures — Kafka, CQRS, event store patterns. This is a key capability for the Phase 2 migration.

  • Cloud data platforms: Experience with at least one major cloud data warehouse (Snowflake, BigQuery, Redshift) and its surrounding ecosystem.

  • SQL: Expert-level SQL. Data architecture without SQL fluency is not architecture, it is advice.

  • Payments domain (preferred): Experience designing data infrastructure for payment systems, financial services, or regulated industries. Understanding of transaction data, settlement records, and reconciliation data structures.

  • Data governance: Experience with data cataloguing, data lineage, data quality frameworks, and regulatory data obligations.

  • Communication: Ability to explain data architecture decisions to non-technical stakeholders — and to enforce data standards with engineers who have different opinions.

General Requirements

  • Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.

  • 8+ years of experience in data architecture, database design, or data warehousing, with at least 3 years in an architecture or lead design role.

  • Demonstrated track record of designing and delivering data platforms that ran in production at scale.

What We Offer

  • Competitive salary benchmarked to your local market.

  • The opportunity to design a data platform from near-greenfield — the transactional systems exist, the analytical infrastructure largely does not yet.

  • Direct collaboration with the AI Principal and Data Scientists on the ML platform design.

  • Transaction data from 7 markets — a genuinely rich and complex analytical environment.

  • Flexible hybrid working.