Senior Data Engineer at eHealth Systems Africa - MY NGO JOBS

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About the Organization

eHealth Africa is focused on improving healthcare by creating effective ways to implement reliable health information management systems. We have developed eHealth and mHealth solutions that can be rapidly deployed to manage patient information, streamline clinical procedures, and provide data and analysis on health program outcomes.

About the Job

Experience: 5 years
Location: Abuja , Kano

Purpose of the position

The Global Health Monitoring (GHM) unit at eHA focuses on innovative solutions like remote temperature monitoring devices to transform cold chain management. This aligns with eHA’s mission to build stronger health systems.
The Senior Data Engineer carries out the design and implementation of data engineering functions within a data-first organization. The focus is on building data pipelines and effectively communicating technical tradeoffs to non-technical audiences. The provision of these services is critical to ensuring the organization’s capacity to thrive in a fast-paced, complex and ambiguous environment while adapting to emerging data needs.

What you’ll do

To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. Other duties may be assigned:

Architect, build, and maintain eHA GHM’s data warehouse from scratch, ensuring the team is fully informed of the outputs. This enables seamless integration with related functions, accelerates Data Science and Business Analyst research, and addresses challenges to sustaining ongoing data-driven outputs
Own data pipelines and integrations – from sources such as eHA GHM transactional and IoT data, software vendor data, and partner or purchased data – for consumption by the data team.
Coordinate with the production backend team to efficiently manage data pipelines and integrations for two systems with different requirements, sustaining data pipeline execution by advocating for the data team’s needs and incorporating evolving requirements from both the data and backend/product teams
Manage Databricks data instance for scalability, security, and performance.
Develop and optimize ETL pipelines for structured and unstructured data ingestion.
Automate data transformation and cleaning to ensure consistency and quality.
Implement geo-tagging workflows to integrate location-based data.
Ensure data governance and compliance with security protocols and regulatory standards
Maintain expert knowledge of emerging data engineering tools, architectures, and processes, evaluate their tradeoffs, and evaluate the data team’s need to adopt them. Lead the development and ownership of eHA GHM’s data engineering roadmap, ensuring solutions align with established data quality, efficiency, security, and compliance standards.
Create and maintain a data dictionary, ensuring consistent data meaning, integrity, and alignment within the data warehouse.
Lead and mentor a data engineering team, ensuring the adoption of best practices and fostering a culture of continuous improvement in performance, scalability, and quality
Adhere to eHealth Africa Code of Conduct as well as ethical standards of the field.

Who you are

Data Engineering: Expertise in architecting, building, and maintaining data systems, including data warehouses and pipelines.
Cloud Solutions: Strong knowledge of cloud data solutions (preferably AWS or GCP) for scalable data architecture.
Data Modeling & ETL: Proficient in designing data models and managing ETL processes.
Distributed Computing: Experience with distributed computing tools such as Hadoop, Spark, and EMR.
Programming & Databases: Strong skills in Python, SQL, and relational database management.
Workflow Management: Knowledge of DAG workflow management tools for orchestrating data pipelines.
Data Governance & Quality: Ensuring data quality, consistency, and integrity across all systems.
Leadership & Team Collaboration: Proven ability to lead teams, collaborate across functions, and communicate complex technical concepts to non-technical stakeholders.

Qualifications and Experience:

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field (or equivalent practical experience).
5+ years of experience in data engineering and working with data technologies.
3+ years of experience architecting cloud-based data solutions, preferably using AWS or GCP.
1+ year of experience in an engineering management or technical leadership role.
Experience with data warehousing, data modeling, ETL, and distributed computing tools.
Proficiency in Databricks Platform
Proficiency in Python and SQL, with hands-on experience in relational databases.
Familiarity with Terraform or other infrastructure-as-code tools.

Desirables

IoT Data: Experience working with IoT data sources and integrating them into data systems.
NoSQL Databases: Exposure to NoSQL databases and their use in data engineering.
Data Visualization: Familiarity with data visualization tools such as Looker or Tableau.
Industry Experience: Background in global health, supply chain, or financial industries is a plus.
Data Science or Analytics: Knowledge of data science or data analytics practices and tools is a bonus.

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Therefore, even if you read the job description section well we recommend you read it again and make sure you understand it more.

Job description provides a basic rundown of the role. It includes the title, basic responsibilities, and required education and experience

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Among the first things that employers looks in job applications is your cover letter, because it briefly explain all about the candidates in few characters, beginning from the candidates interests about the jobs, educational background, work experience and other supporting documents.

Cover letter should be done to relates with the job description, experience related to the position and requirements.

It is very important to include cover letter in all your job applications.

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It consists a brief description about candidates, educational background, work experiences, Additional training, skills and References.

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