Data Engineer

Data engineering at Paystack focuses on building and extending platforms for managing data at scale. This involves data ingestion, processing, storage and egress. Data engineers are also responsible for creating and maintaining the infrastructure our data platforms run on.

Data engineers operate across a diverse tech stack. They are expected to be adaptable and drawn to learning new skills and technologies.

The role requires a proactive individual who can work independently and collaboratively within a remote-first environment, has a strong software engineering background with good experience building and maintaining data pipelines, expertise in Python and experience in streaming technologies.

• Data Pipeline Development: Design, develop, and maintain robust data pipelines using ETL and ELT methodologies to process and integrate data from various sources into a data lake, a central data warehouse, operational data stores, analytical data marts and various application interfaces.
• Streaming Data Processing: Implement and manage real-time data streaming solutions utilising Kafka, Debezium, Kafka Connect.
• Workflow Orchestration: Build, schedule and maintain custom workflows using Apache Airflow to ensure timely and accurate data processing and delivery.
• Database Management: Work with a variety of database technologies, including relational databases (MySQL, PostgreSQL), NoSQL databases (MongoDB) and analytical/big data systems (Redshift, BigQuery, SingleStore).
• Infrastructure as Code: Employ tools like Terraform, Kubernetes, and Helm to manage and provision infrastructure efficiently.
• CI/CD Implementation: Develop and maintain continuous integration and deployment pipelines to streamline development processes.
• Testing and Quality Assurance: Conduct unit and integration testing to ensure high code quality, data integrity and system reliability.
• Collaboration: Engage with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data needs and deliver solutions.
• Documentation: Maintain clear and comprehensive documentation of data processes, workflows and systems.
• Monitoring and Support: Monitoring system performance and addressing faults and failures in production systems as part of an on-call rotation.

• Educational Background: Bachelor's degree in Computer Science, Engineering or a related field.
• Programming Skills: Proficiency in Python is essential. JavaScript and Scala development experience is advantageous.
• Data Engineering Experience: Minimum of 3 years of experience in data engineering roles, with a focus on building and managing data pipelines.
• Software Engineering Experience: Minimum of 2 years experience in software and/or application development roles (can be concurrent with data engineering experience).
• Streaming Technologies: Hands-on experience with Kafka, Debezium, and Kafka Connect.
• Data pipeline orchestration tools: Proficiency in a data pipeline orchestration tool or suitable workflow orchestration tool like Apache Airflow (preferred), Databricks, Dagster or Airbyte.
• Database Expertise: Strong understanding and hands-on experience working with various database technologies, including MySQL, PostgreSQL, MongoDB and Redshift (BigQuery and SingleStore advantageous).
• Infrastructure Tools: Experience with Terraform, Kubernetes, and Helm for infrastructure management.
• Cloud Computing: Solid knowledge of cloud computing concepts, with experience in AWS services being advantageous.
• SQL Proficiency: Ability to write complex SQL queries across different dialects.
• Testing Practices: Familiarity with unit and integration testing methodologies.
• CI/CD Pipelines: Experience in setting up and maintaining CI/CD pipelines.
• Data Science Tools: Exposure to analytical systems and basic data science tooling. Familiarity with basic machine learning and analytical modelling concepts advantageous.
• BI Reporting Platforms: Exposure to self-service reporting tools like Tableau, Looker and DOMO.