Data Engineer

We are seeking an On -site Senior Data Engineer who would be responsible for designing, building, and maintaining scalable, secure, and high -performance data infrastructure that powers analytics, AI/ML models, and enterprise applications. The role sits at the intersection of <\/span>data engineering, applied machine learning support, and software systems<\/b>, working closely with the Senior AI & Software Manager to translate product, AI, and business requirements into robust data pipelines and platforms. <\/p>

This role is delivery -focused and impact -driven, with strong ownership of data reliability, performance, and governance across cloud and distributed environments. <\/p> The Ideal Candidate should be able to; <\/div> • Design, develop, and maintain <\/span>end -to -end ETL/ELT pipelines<\/b> <\/span>for structured and unstructured data using Python and SQL. <\/p><\/li> • Build scalable batch and near -real -time data workflows leveraging <\/span>Apache Spark, Hadoop, Kafka, and Airflow<\/b>. <\/p><\/li> • Implement data ingestion, transformation, validation, and enrichment pipelines across multiple data sources (APIs, files, databases, streaming systems). <\/p><\/li>

Ensure high data quality through automated checks, anomaly detection, and validation logic, including ML -assisted data quality monitoring. <\/p><\/li><\/ul> Cloud Data Platforms & Warehousing <\/h3>

Architect and manage cloud -based data solutions across <\/span>AWS (S3, Glue, Redshift, EMR), GCP (BigQuery, Dataflow, Pub/Sub), and Azure (Data Factory)<\/b>. <\/p><\/li>

Design and optimize <\/span>data warehouses and analytical data models<\/b> <\/span>to support BI tools, AI workflows, and operational analytics. <\/p><\/li>

Implement cost -efficient storage and compute strategies while maintaining performance and scalability. <\/p><\/li><\/ul> AI & Machine Learning Enablement <\/h3>

Work closely with the Senior AI & Software Manager to <\/span>prepare, structure, and optimize datasets<\/b> <\/span>for machine learning and predictive analytics. <\/p><\/li>

Support ML pipelines by enabling feature engineering, training data generation, and inference -ready data flows. <\/p><\/li>

Collaborate on integrating ML outputs into production systems and dashboards. <\/p><\/li>

Ensure data pipelines align with AI model requirements for freshness, latency, and reliability. <\/p><\/li><\/ul> Software & API Integration <\/h3>

Develop and maintain data services and APIs using <\/span>FastAPI, Django REST, or Flask<\/b> <\/span>to expose data to applications and AI systems. <\/p><\/li>

Collaborate with software engineers to integrate data pipelines into broader system architectures. <\/p><\/li>

Ensure data platforms align with software engineering best practices (modularity, versioning, CI/CD readiness). <\/p><\/li><\/ul> Analytics, Reporting & Decision Support <\/h3>

Enable downstream analytics and reporting through clean, well -modeled datasets. <\/p><\/li>

Support BI and visualization tools such as <\/span>Power BI and Looker<\/b> <\/span>by delivering optimized datasets and semantic layers. <\/p><\/li>

Partner with stakeholders to translate analytical and operational needs into technical data requirements. <\/p><\/li><\/ul> Governance, Security & Compliance <\/h3>

Implement data governance standards, access controls, and compliance measures, particularly for sensitive or regulated datasets. <\/p><\/li>

Ensure data integrity, traceability, and auditability across pipelines and storage layers. <\/p><\/li>

Collaborate on defining data documentation, lineage, and metadata practices. <\/p><\/li><\/ul> Collaboration & Leadership <\/h3>

Act as a senior technical partner to the <\/span>Senior AI & Software Manager<\/b>, contributing to architectural decisions and system design discussions. <\/p><\/li>

Collaborate with data scientists, AI engineers, software developers, and non -technical stakeholders. <\/p><\/li>

Provide technical guidance and mentorship to junior data engineers or analysts when required. <\/p><\/li>

Participate in planning, estimation, and delivery of complex data -driven projects. <\/p><\/li><\/ul>

<\/div><\/span> Requirements<\/h3>

Strong proficiency in <\/span>Python and SQL<\/b> <\/span>for data engineering and analytics. <\/p><\/li>

Hands -on experience with <\/span>Apache Spark, Hadoop, Kafka, and Airflow<\/b>. <\/p><\/li>

Solid understanding of <\/span>ETL/ELT design patterns<\/b>, data modeling, and warehousing. <\/p><\/li>

Experience with <\/span>cloud data platforms<\/b> <\/span>(AWS, GCP, Azure). <\/p><\/li>

Familiarity with <\/span>machine learning workflows<\/b>, including data preparation and feature engineering. <\/p><\/li>

Experience building APIs and services using <\/span>FastAPI, Django REST, or Flask<\/b>. <\/p><\/li>

Working knowledge of <\/span>Docker, Kubernetes, and Git<\/b>. <\/p><\/li>

Experience supporting BI tools such as <\/span>Power BI or Looker<\/b>. <\/p><\/li><\/ul> Professional Experience <\/h3>

Proven experience delivering <\/span>large -scale, production -grade data systems<\/b>. <\/p><\/li>

Experience working on <\/span>multi -stakeholder, high -impact projects<\/b>, including government or enterprise environments. <\/p><\/li>

Demonstrated ability to reduce processing time, improve data quality, and scale data operations. <\/p><\/li>

Track record of translating business or AI requirements into reliable technical solutions. <\/p><\/li><\/ul> Education & Background <\/h3>

Degree in Engineering, Computer Science, Statistics, or a related technical field. <\/p><\/li>

Formal training or certification in <\/span>Data Science, Big Data, or Machine Learning<\/b> <\/span>is a strong advantage. <\/p><\/li><\/ul>

<\/p> SHOULD BE BASED IN ABUJA OR WILLING TO RELOCATE. <\/div> <\/div><\/span>