TB-Scale Data Platform Engineer – Hybrid (Cape Town)

impact.com; Cape Town; Hybrid; ZAR 850,000 - 1,250,000; Full time

Benefits offered by this job

Flexible Working

Health and Wellness

RSUs with 3-year vesting

Coursera subscription

Parental Leave

Technology stipend

Job summary

impact.com in Cape Town seeks a Data Platform Engineer to own production pipelines and platform components at TB-scale, delivering data reliably, securely, and cost-effectively as the business grows.

Design and build scalable ETL/ELT and streaming pipelines with Spark/Dataproc, Kafka, and BigQuery; ensure SLOs, on-call readiness, and governance while collaborating closely with Analytics Engineers, Data Analysts, and Data Scientists.

Qualifications

• 3–5 years in data platform, data engineering, or distributed systems with TB-scale production
• Strong Python and production JVM experience; familiarity with Scala
• Advanced SQL and data governance practices
• Experience with Airflow or similar orchestration tools
• Cloud-based data platforms (GCP preferred) and BigQuery experience
• Mentorship and clear written/verbal communication skills

Responsibilities

• Own platform components and pipelines end-to-end, including design, implementation, reliability, and cost
• Configure and maintain core platform infrastructure as code (Dataproc, Kafka, Airflow/Astronomer, BigQuery)
• Own SLOs for freshness, latency, and success; participate in on-call as Tier 2
• Implement security, governance, access control, and data lineage by design
• Write clean, well-tested code with CI/CD and current runbooks/architecture docs
• Mentor Associate Data Platform Engineers through pairing and code review

Skills

Python

Scala

SQL

Cloud platforms

JVM languages

Education

Bachelor's degree in CS/Engineering/Math

Tools

Airflow

Astronomer

BigQuery

Dataproc

Kafka

Pub/Sub

dbt

Looker

Job description

impact.com in Cape Town seeks a Data Platform Engineer to own production pipelines and platform components at TB-scale, delivering data reliably, securely, and cost-effectively as the business grows.

Design and build scalable ETL/ELT and streaming pipelines with Spark/Dataproc, Kafka, and BigQuery; ensure SLOs, on-call readiness, and governance while collaborating closely with Analytics Engineers, Data Analysts, and Data Scientists.