Credit Direct Limited is a non-bank finance company with its Head-Quarters in Lagos, Nigeria. The company was established in 2006 and is focused on providing Payroll based consumer loans to eligible individuals. The Company currently operates in 25 states in Nigeria including the Federal Capital Territory– Abuja. With a staff strength of over 1000 employees and an active customer base in excess of 300,000, Credit Direct Limited is positioning itself to become the dominant market leader in the unsecured micro-lending (payroll lending) space in Nigeria and indeed Sub-Saharan Africa.
Job Summary
- We are looking to fill the role of an Analytics Engineer. The role holder will be responsible for developing, maintaining, and optimizing data pipelines, ensuring data quality, and supporting analytics initiatives. This role requires expertise in data modelling, data transformation, and data governance to generate insights that support business decision-making.
Job Details
RESPONSIBILITIES:
Data Modelling & Schema Design:
- Develop and implement data models that support analytics and reporting requirements, ensuring scalability, performance, and data accuracy.
- Work with stakeholders to translate business requirements into logical and physical data models, creating well-structured schemas that align with company standards.
Data Infrastructure Management:
- Optimize database structures (e.g., indexing, partitioning) to enhance query performance and improve data accessibility.
- Ensure data reliability, security, and scalability to support business needs and data-driven applications.
Analytics & Reporting Support:
- Develop and maintain dashboards, reports, and other visualizations to deliver insights that drive key business decisions.
- Collaborate with data analysts to translate business questions into metrics and key performance indicators (KPIs).
- Continuously improve reporting capabilities to support evolving business objectives.
Data Quality & Governance:
- Implement data governance and security measures to protect sensitive information.
- Maintain documentation for data sources, transformations, and definitions to facilitate consistent data usage.
Requirements
- Bachelor’s degree in Computer Science, Engineering, or a related field. Equivalent experience may be considered.
- 1 – 3+ years in data engineering, analytics engineering, data analyst, or a similar field.
- Proven experience with SQL databases (MySQL, MSSQL, BigQuery, and Amazon RDS) and NoSQL databases (e.g, MongoDB)
- Experience with data transformation tools such as dbt, AWS glue, Dataform, and scripting languages like Python.
- Proven experience with workflow orchestration tools like Apache Airflow, Prefect, Dagster, and data integration tools like Airbyte.
- Prior experience with cloud platform, particularly GCP and/or AWS
COMPETENCIES REQUIREMENTS:
Technical:
- Database Management & Modeling: Proficiency in data pipeline development, data warehousing, data modelling, and transformation using dbt and other related transformation tools.
- Cloud Infrastructure Management: Strong understanding of cloud data management in GCP and AWS.
- Strong knowledge of and experience with Visualization and business intelligence tools like PowerBI, Tableau, or Looker studio.
- Database Management: Experience in SQL (BigQuery, Amazon RDS) and NoSQL databases (MongoDB, DynamoDB).
- Programming & Scripting: Proficiency in SQL and Python for data processing and automation.
- Experience working with financial data is a plus.
Tools:
- SQL, dbt (Data Build Tool), Python, Airbyte, Apache Airflow, BigQuery, Amazon RDS, MongoDB, PowerBI/Tableau, Terraform
Behavioural:
- Analytical Skills: Ability to solve complex data problems and improve data pipeline efficiency.
- Collaboration: Skilled in working cross-functionally with data analysts, engineers, and business stakeholders.
- Attention to Detail: Accuracy in data transformations and adherence to data governance standards.
- Communication: Strong verbal and written communication skills to document processes and share insights.
What to Expect in the Hiring Process:
- A preliminary phone call with the recruiter
- Technical interview
- Assessment
- Interview with Senior members of the team
- Cultural and Behavioural Fit Interview with a member of the Executive team.
Method of Application
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