Data Engineer

Dariel; Gauteng; On-site; ZAR 900,000 - 1,200,000; Full time

Job summary

Dariel is seeking engineers who blend strong software fundamentals with modern data engineering to design, build, deploy and operate cloud-native data platforms. The role emphasizes SQL and warehousing alongside broader engineering capabilities, leveraging Spark, Databricks, Airflow, and cloud services.

Ideal candidates will deliver scalable data solutions, contribute to production systems, and follow established engineering standards across the delivery lifecycle.

Qualifications

• Proficiency in Python and SDLC practices.
• Strong SQL and data warehousing experience.
• Experience with APIs and code collaboration workflows.
• Hands-on with cloud-native data platforms and data lake architectures.

Skills

Python

SDLC

Git

APIs

Data Modeling

Cloud Platforms

Spark

Databricks

Airflow

SQL

Tools

Terraform

Docker

Kubernetes

Databricks

Job description

Role Summary

We are seeking engineers who combine strong software engineering fundamentals with modern data engineering expertise. Candidates should be capable of designing, building, deploying and operating cloud-native data platforms while applying software engineering best practices throughout the delivery lifecycle.

Strong SQL and data warehousing experience remain important but should complement broader engineering capability rather than define the candidate's profile.

Minimum Technical Requirements (Non-Negotiable)

Candidates must demonstrate practical project experience with:

Software Engineering

• Python as a primary programming language
• Software engineering principles and clean coding practices
• Object-oriented programming
• Testing and code quality practices
• Software Development Life Cycle (SDLC)
• Git and collaborative development workflows
• API development and integration

Candidates should demonstrate the ability to:

• Solve business problems through code
• Design scalable solutions
• Work within engineering teams
• Contribute to production systems
• Follow engineering standards and best practices

Core Data Engineering Requirements

Candidates should have hands-on experience with several of the following:

Data Processing

• Spark
• PySpark
• Databricks
• Data Lake architectures
• Batch processing
• Streaming architectures
• Data transformation frameworks

Data Platform Technologies

• Kafka
• Flink
• Airflow
• Modern orchestration platforms

Data Storage & Analytics

• SQL
• Data warehousing concepts
• Relational databases
• Analytical data platforms

Candidates should have practical experience delivering solutions on at least one major cloud platform:

Preferred Order

• Amazon Web Services (AWS)

Typical Technologies

GCP

• BigQuery
• Dataflow
• Pub/Sub
• GKE

AWS

• Glue
• EMR
• Redshift
• Kinesis
• EKS
• S3
• Data Factory
• Synapse
• Databricks
• Event Hubs
• AKS

Cloud experience should reflect real project delivery rather than certifications alone.

Strong candidates should also demonstrate exposure to:

Containerisation & Deployment

• Docker

Infrastructure

• Terraform
• Infrastructure as Code

Operations

• Monitoring
• Observability
• Production support

Candidates should possess working knowledge of:

Data Warehousing

• Data warehouse design
• Data governance concepts

Data modelling should support modern platform development, not exist in isolation.

Ideal Candidate Profile

The strongest candidates will demonstrate: