Data Engineer

Talent Safari

3d•Remote

About The Position

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 on which our data platforms run. 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.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field.
Proficiency in Python is essential.
Minimum of 3 years of experience in data engineering roles, with a focus on building and managing data pipelines.
Minimum of 2 years of experience in software and/or application development roles (can be concurrent with data engineering experience).
Hands-on experience with Kafka, Debezium, and Kafka Connect.
Proficiency in a data pipeline orchestration tool or suitable workflow orchestration tool like Apache Airflow (preferred), Databricks, Dagster, or Airbyte.
Strong understanding and hands-on experience working with various database technologies, including MySQL, PostgreSQL, MongoDB, and Redshift (BigQuery and SingleStore advantageous).
Experience with Terraform, Kubernetes, and Helm for infrastructure management.
Solid knowledge of cloud computing concepts, with experience in AWS services being advantageous.
Ability to write complex SQL queries across different dialects.
Familiarity with unit and integration testing methodologies.
Experience in setting up and maintaining CI/CD pipelines.
Exposure to analytical systems and basic data science tooling.
Exposure to self-service reporting tools like Tableau, Looker, and DOMO.
Good verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
Demonstrated ability to work collaboratively within a team and across departments.
Comfortable working in a fast-paced environment with changing priorities, technologies, and tooling.
Strong analytical and problem-solving skills.

Nice To Haves

Knowledge of JavaScript and Scala is advantageous.
Familiarity with basic machine learning and analytical modelling concepts advantageous.

Responsibilities

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.
Implement and manage real-time data streaming solutions utilising Kafka, Debezium, and Kafka Connect.
Build, schedule, and maintain custom workflows using Apache Airflow to ensure timely and accurate data processing and delivery.
Work with a variety of database technologies, including relational databases (MySQL, PostgreSQL), NoSQL databases (MongoDB), and analytical/big data systems (Redshift, BigQuery, SingleStore).
Employ tools like Terraform, Kubernetes, and Helm to manage and provision infrastructure efficiently.
Develop and maintain continuous integration and deployment pipelines to streamline development processes.
Conduct unit and integration testing to ensure high code quality, data integrity, and system reliability.
Engage with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand data needs and deliver solutions.
Maintain clear and comprehensive documentation of data processes, workflows, and systems.
Monitoring system performance and addressing faults and failures in production systems as part of an on-call rotation.