Forward Deployed Engineer; Cloud & Data)
Job in
Johannesburg, 2000, South Africa
Listed on 2026-10-09
Listing for:
Badger & Bird Talent
Full Time
position Listed on 2026-10-09
Job specializations:
-
IT/Tech
Data Engineering, Cloud Computing: Infrastructure & Operations, AWS
Job Description & How to Apply Below
Most software engineering roles force you into a box: you either build standard backend APIs all day or get stuck buried inside data pipeline maintenance. This position puts you right at the intersection of both, embedding you directly into client operations to solve complex cloud and data engineering bottlenecks where standard off-the-shelf software fails.
What is in it for you
- Compensation: Up to R110,000 pm, aligned with your technical depth and experience.
- Work Setup: Remote-first flexibility. South Africa based candidates are highly preferred so occasional in-person sessions are quick and hassle-free, but anyone within a GMT±2 time zone will be considered.
- Growth & Equipment: Fully funded cloud and data certifications, company-supplied hardware setup, and performance-based cash bonuses.
- Direct Engineering Impact: Work on live client architectures without getting dragged into theoretical slide decks or corporate bureaucracy.
What you will be doing
- Bespoke Backend Engineering: Write clean, scalable microservices in Python, Go, Java, or Node.js to fix operational bottlenecks.
- Infrastructure as Code: Provision and secure multi-cloud infrastructure across AWS, Azure, or GCP using Terraform or Open Tofu.
- Data Streaming & Pipelines: Build real-time streaming and batch pipelines using Kafka, Spark, Databricks, Snowflake, and dbt.
- Container Orchestration: Package, deploy, and manage production workloads at scale using Docker and Kubernetes (EKS, AKS, GKE).
What you need to bring
- Solid production coding experience in Python, Go, Java, or Node.js.
- Hands‑on expertise writing Infrastructure as Code (Terraform, Ansible, or Cloud Formation) across AWS, Azure, or GCP.
- Practical background building data pipelines (Kafka, Spark, Databricks, Snowflake) and running containerized workloads (Docker, Kubernetes) in production.
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