Data Engineer
The Mission
We are a rapidly expanding Kenyan startup that has successfully secured over $5 million in funding to scale our operations across the African continent.
We are seeking a Data Engineer to spearhead the architecture and optimization of the data foundation that underpins our core optimization platform. In this role, you will partner with our engineering and analytics squads to construct resilient, high-efficiency data pipelines that fuel our machine learning models and internal analytics.
Your technical contributions will be the backbone of our decision-making process, ensuring the business relies on high-integrity, available data.
What You Will Do
1. Infrastructure Architecture & Evolution
Pipeline Engineering: Architect and sustain scalable ETL workflows, guaranteeing consistency and accuracy across diverse data origins.
Database Strategy: Refine and optimize data models and database structures specifically tailored for reporting and analytics.
Storage Standards: Enforce industry best practices regarding data warehousing and storage methodologies.
System Efficiency: Fine-tune data systems to handle the demands of both real-time streams and batch processing.
Cloud Ecosystem: oversee and manage the cloud data environment, utilizing platforms such as AWS, Azure, or GCP.
Product Integration: Coordinate with software engineers to embed data solutions directly into our product suite.
2. Data Operations & Integrity
Ingestion Logic: Design robust processes for ingesting both structured and unstructured datasets.
Quality Assurance: script automated quality checks and deploy monitoring instrumentation to instantly detect data anomalies.
Connectivity: Build APIs and services that ensure seamless data interoperability between systems.
Reliability: Continuously monitor pipeline health, troubleshooting bottlenecks to maintain an uninterrupted data flow.
Compliance: Embed data governance and security protocols that meet rigorous industry standards.
3. Cross-Functional Synergy
Enablement: Collaborate with data scientists and analysts to maximize the usability and accessibility of our data assets.
Knowledge Management: Maintain comprehensive documentation covering schemas, transformations, and pipeline architecture.
Innovation: Keep a pulse on emerging trends in cloud tech, analytics, and data engineering to drive continuous improvement.
What You Have
Technical Background
Education: Bachelor’s or Master’s degree in Engineering, Computer Science, Data Science, or a relevant discipline.
Experience: A minimum of 3 years of professional experience in Data Engineering or a similar technical role.
Database Fluency: Expert-level command of SQL and management systems like PostgreSQL or MySQL.
Orchestration: Hands-on proficiency with pipeline tools such as Luigi, DBT, or Apache Airflow.
Big Data: Practical experience with heavy-lifting technologies like Hadoop, Spark, or Kafka.
Cloud Native: Proven skills with cloud data stacks, specifically Google BigQuery, AWS Redshift, or Azure Data Factory.
Coding: Strong programming logic in Java, Scala, or Python for data processing tasks.
Integration: Familiarity with data integration frameworks and API utilization.
Governance: Understanding of security best practices and compliance frameworks.
Professional Attributes
Analytical Mindset: Exceptional problem-solving capabilities with a rigorous eye for detail.
Communication: The ability to collaborate effectively and communicate complex ideas clearly.
Agility: Comfortable navigating a high-velocity environment while juggling competing priorities.
Ownership: A proactive, self-starting attitude with a deep sense of accountability.
Impact-Driven: A genuine enthusiasm for leveraging data to unlock tangible business value.