Associate Data Platform Engineer

is the world’s leading commerce partnership marketing platform, transforming the way businesses grow by enabling them to discover, manage, and scale partnerships across the entire customer journey. From affiliates and influencers to content publishers, brand ambassadors, and customer advocates,  empowers brands to drive trusted, performance-based growth through authentic relationships. Its award-winning products—
Performance (affiliate),
Creator (influencer), and Advocate (customer referral)—unify every type of partner into one integrated platform. As consumers increasingly rely on recommendations from people and communities they trust,  helps brands show up where it matters most. Today, over 5,000 global brands, including Walmart, Uber, Shopify, Lenovo, L’Oréal, and Fanatics, rely on  to power more than 225,000 partnerships that deliver measurable business results.

Impact's Data Platform Engineering team is looking for an Associate Data Platform Engineer ready to build real distributed systems skills on large-scale production data. Our platform powers analytics, ML, and data products at TB–PB scale using Scala, Python, Spark, Kafka, Airflow, and Big Query on GCP.

This is a foundational role for someone early in their career who wants to learn how serious data infrastructure is built and operated, with mentorship from experienced engineers and clear patterns to follow. You'll work with in the Data Analytics Group (DAG) alongside Analytics Engineers, Data Analysts, and Data Scientists, though you won't own dbt models, metric definitions, or reporting. Our ideal candidate has strong programming and SQL fundamentals, genuine curiosity about how systems behave under load, and good judgement about when to ask for help.

The role reports to the Team Lead, Data Platform Engineering, and is based in Cape Town, hybrid, with two days per week in office.

The platform has strong foundations:
Scala and Spark pipelines processing TB-scale data, dozens of managed data connectors, Airflow on Astronomer for orchestration, a mature dbt transformation layer, and Big Query as the core analytical warehouse. You'll also have room to shape the platform's next chapter: modernising parts of the compute stack, and building out governance frameworks, data contracts, SLOs, observability, cost management, and data quality monitoring within your domain.

• Pipeline development: Build and extend batch and streaming pipeline components under guidance. Write clean, tested Scala and Python code. Add Spark transformation steps, build Airflow DAGs, and implement Kafka consumers with senior support.
• Platform configuration: Configure platform components such as Big Query tables, partitioning strategies, and Cloud Storage buckets with guidance.
• Observability & data quality: Build monitoring dashboards and alerting for job success, latency, and pipeline health. Implement data quality checks to catch anomalies, schema violations, and missing data.
• Operations & incident response: Shadow the on-call rotation, help investigate root causes, debug using logs, metrics, and traces, and maintain runbooks and operational documentation.
• Security, governance & cost awareness: Follow least-privilege access, secrets management, and PII handling by default. Consider the cost and performance implications of your changes with guidance.
• Collaboration & craft: Use AI coding assistants while holding the same quality and review standards. Participate in code reviews, pairing, and technical discussions, and communicate progress and blockers clearly.

Preferred Requirements:

• 1–3 years of experience in software engineering, data engineering, or a related technical role
• Working programming ability in…