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Data Engineer – Analytics & Data Infrastructure
The Khana Group
Posted 4 months agoNationwide (Hybrid)
Location
Nationwide (Hybrid)
Job Type
Full-time
Experience
Mid-Level
Category
Software & Data
Job Description
The Khana Group (FieldWorkz) is seeking a Data Engineer to design and manage the backbone of our data systems. As we scale, we must strengthen our data infrastructure to support automation, multi-country datasets, and enterprise-grade analytics. This is a high-impact hybrid role with nationwide coverage across Nigeria. Remuneration: NGN 800,000 – 1.2 Million Net per month.
Data Pipeline Development: Design, build, and maintain scalable ETL and ELT pipelines.
Data Cleaning & Structuring: Develop automated scripts for cleaning, validation, and transformation of datasets. Standardise data schemas across projects and countries.
Data Architecture & Optimisation: Design data storage solutions optimised for analytics and large datasets.
Automation & Performance: Build repeatable workflows for recurring reporting and dashboard products.
Data Governance & Security: Support secure handling of sensitive data in compliance with data protection standards.
4 or more years of experience in data engineering or data architecture roles. Strong proficiency in SQL. Experience with Python or similar data processing languages. Experience designing and maintaining ETL pipelines. Familiarity with cloud data platforms such as BigQuery, Redshift, Snowflake, or similar. Experience handling survey, research, or multi-country datasets is preferred. Strong understanding of data modelling and schema design.
Competitive salary and performance incentives. Equity or profit-sharing opportunities. Global exposure and high-impact work across Africa. A collaborative and ambitious work culture rooted in Community, Tribe, Innovation, and Excellence. Flexible work options and hybrid working arrangements.
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Responsibilities
- Implement indexing, partitioning, and query optimisation strategies.
- Improve data retrieval speed and reduce compute costs.
- Ensure scalable architecture that supports future product expansion.
- Reduce turnaround time between data collection and analytics delivery.
- Collaborate with product and engineering teams to align data structures with platform needs.
- Implement structured access controls and audit tracking.
- Maintain documentation of data architecture and workflows.
- Automate ingestion of data from survey platforms, APIs, field tools, and internal systems.
- Ensure reliable batch and near real-time data processing.
- Implement monitoring and alert systems for pipeline health.
- Ensure data consistency and integrity across all platform layers.
- Reduce manual data preparation and cleaning efforts.