Data Engineer at Inkomoko

Data Engineer at Inkomoko Builds and maintains scalable data pipelines and analytics infrastructure. Requires Computer Science Degree, strong SQL/Python skills, and 2+ years’ data engineering experience. Full-time.

Job Overview

As a Data Engineer, you will build and operate the pipelines at the heart of Inkomoko’s enterprise data platform, ensuring trusted, secure and scalable data is available to support analytics, AI, products, operations and organisational decision-making. You will work within the technical architecture and engineering standards set by the Senior Data Engineer, with growing ownership as you demonstrate it.

This is a role for an engineer who wants to get significantly better, fast, with structured mentorship. You will be mentored by the Senior Data Engineer through code review, pairing, and a written growth plan, and you will work on one of the hardest practical problems in our estate: contributing to the client entity resolution layer that joins the same person across the advisory, core banking, and survey systems, with transparency, auditability and measurable data quality.

You will work with data about refugee entrepreneurs. That carries real responsibility: every pipeline you touch operates under Inkomoko’s data protection controls and ingests only the columns the business needs, and all data movement and integration must comply with approved enterprise architecture, security, privacy and data-governance standards.

Key Responsibilities

Pipeline Development and Maintenance (40%)

• Build, maintain and operate reliable, auditable data pipelines integrating approved enterprise, lending, programme, survey and external data sources, within the platform architecture set by the Senior Data Engineer.
• Keep all loads idempotent and checkpointed, so an interrupted run resumes rather than restarts, and document every pipeline as code alongside the pipeline itself.
• Enforce column-level minimization at ingestion: personal data that no downstream model needs is never ingested.
• Apply the platform’s reusable integration patterns when onboarding new sources, and flag where a pattern does not fit rather than working around it silently.

Data Quality and Reliability (30%)

• Implement tests, contracts, and freshness checks for the pipelines you own, feeding the promotion-blocking quality gate: when a check fails, downstream publication halts and someone is alerted, by design.
• Monitor the health, performance and cost of your pipelines through the platform’s observability and alerting, and respond to incidents within agreed SLAs/SLOs.
• Participate in root-cause analysis for pipeline and gate failures, and contribute short, blameless written causes and fixes.

Entity Resolution and Integration Support (15%)

• Implement components of the client entity resolution layer to the Senior Data Engineer’s design, including matching logic, survivorship rules, and the auditable record of merge decisions.
• Investigate unresolved or conflicting matches and surface them with evidence, rather than silently picking one.

Governance, Security and Collaboration (15%)

• Apply the technical controls required by Inkomoko’s Data Governance Framework in the pipelines you build, including classification, access, lineage, retention, encryption, minimisation and auditability.
• Work within the platform’s data protection controls, aligned with the data protection laws of the jurisdictions we operate in; access administration is centrally managed and this role works inside it, not above it.
• Collaborate with Technology Enablement, Product and Innovation, MERL and business system owners on source-system changes and data dependencies affecting your pipelines.
• Engage actively in mentorship: structured code review, pairing, and a written growth plan with the Senior Data Engineer.

Requirements

• Education: Bachelor’s degree in Computer Science, Engineering, Statistics, or a related field; equivalent demonstrated experience considered.
• Experience: 2 to 4 years in data engineering or backend engineering with substantial data work, in a production environment.
• Programming: strong Python and SQL; you write pipelines as software, with tests, not as scripts.
• Databases: solid PostgreSQL; exposure to SQL Server is a plus.
• Transformation and orchestration: hands-on dbt experience and familiarity with a modern orchestrator; Dagster preferred, Airflow background welcome.
• Data movement: working understanding of incremental and idempotent load patterns; familiarity with Kafka and Spark is a valued plus, not a requirement.
• Engineering practice: Git discipline, CI/CD, and containerization (for example Docker) as daily habits; documentation you would want to inherit.
• Data protection: awareness of handling sensitive personal data responsibly; experience with regulated, financial, or humanitarian data is a strong plus.
• Growth: evidence that you seek and act on feedback; this role comes with structured mentorship, and we are looking for someone who will use it.
• Communication: ability to explain what a pipeline does, what broke, and what you changed, clearly and in writing.

How to Apply

Click here to apply