Our Company:
At impact.com we are passionate about our people, our technology, and are obsessed with customer success. Working together enables us to grow rapidly, win, and serve the largest brands in the world. We use cutting edge technology to solve real-world problems for our clients and continue to pull ahead of the pack as the leading SaaS platform for businesses to automate their partnerships and grow their revenue like never before. We have an entrepreneurial spirit and a culture where ambition and curiosity is rewarded. If you are looking to join a team where your opinion is valued, your contributions are noticed, and enjoy working with fun and talented people from all over the world, then this is the place for you!
impact.com, the world’s leading partnership management platform, is transforming the way businesses manage and optimize all types of partnerships—including traditional rewards affiliates, influencers, commerce content publishers, B2B, and more. The company’s powerful, purpose-built platform makes it easy for businesses to create, manage, and scale an ecosystem of partnerships with the brands and communities that customers trust to make purchases, get information, and entertain themselves at home, at work, or on the go. To learn more about how impact.com’s technology platform and partnerships marketplace is driving revenue growth for global enterprise brands such as Walmart, Uber, Shopify, Lenovo, L’Oreal, Fanatics and Levi’s, visit www.impact.com.
Your Role at impact.com:
impact.com's Analytics Data Platform processes terabyte-scale data for one of the world's leading partnership SaaS platforms, covering the full lifecycle from ingestion to governed access.
We're looking for a technical people leader to own and grow the Data Platform Engineering team, while driving the architecture, reliability, and evolution of impact.com's Analytics Data Platform. This is a senior player-coach role: you'll combine hands-on technical leadership with direct people management, building a high-performing team, owning delivery, and shaping platform strategy. Expect to spend roughly 40% hands-on and 60% on leadership, people, and delivery, shifting as the team matures. You'll be accountable for both the team's output and its people, ensuring engineers are supported, challenged, and growing.
As Team Lead for Data Platform Engineering, you'll work across the full data ecosystem (ingestion, processing, storage, orchestration, access, and governance) using technologies like Scala, Python, Google Cloud Dataproc, BigQuery, and Airflow. You'll shape the operating model for the team, establish engineering standards, and drive platform strategy. This role is levelled at Senior/Staff Engineer on the engineering management track and reports to the Director of Advanced Analytics. The position is based in Cape Town, hybrid, with one day per week in office.
Data Platform Engineering is one of four sub-teams within the Data Analytics Group (DAG). DPE owns ingestion, infrastructure, orchestration, and platform services. Analytics Engineering owns the dbt transformation layer and semantic layer. Data Analytics owns reporting and insights. Data Product Management owns product roadmaps and governance processes. You'll partner closely with all three, but won't own dbt models, reporting, or product prioritisation directly.
Our ideal candidate combines distributed systems expertise with proven people leadership. Someone who can make architecture decisions, run a team, and translate platform trade-offs into business language. This role is for someone who wants to build both a platform and a team, not just maintain what exists.
What you'll inherit:
The platform has strong foundations: Scala/Spark pipelines processing TB-scale data, 37+ Fivetran connectors, Airflow on Astronomer for orchestration, a mature dbt-based transformation layer, and BigQuery as the core analytical warehouse. Databricks is part of the current stack but is being transitioned off in favour of Dataproc and BigQuery-native patterns. What's greenfield: governance frameworks, data contracts, SLOs/SLAs, observability, cost management, and data quality monitoring. You'll build the team that delivers on these opportunities.
What you'll do:
- Build and run a team of data platform engineers: set direction, remove blockers, give honest feedback, and make sure people are growing in their careers.
- Create an environment where engineers do their best work: high trust, high standards, low friction.
- Turn platform strategy into a delivery plan the team can actually execute: sequence the work, balance new builds against keeping the lights on, and make the trade-offs visible.
- Own capacity and priorities across the team. Keep cross-team dependencies from becoming bottlenecks.
- Lead platform architecture decisions, design reviews, and cross-org technical coordination, defining and promoting architectural patterns, standards, and guidelines.
- Design and implement scalable pipelines using Scala, Python, and distributed frameworks (Spark on Dataproc) processing TB-scale data. Lead the migration off Databricks onto Dataproc and BigQuery-native patterns.
- Establish platform reliability practices: define SLOs/SLAs, build incident management and response processes, and drive systemic improvements across pipeline and infrastructure operations.
- Design and maintain core platform services (Dataproc clusters, BigQuery datasets, Cloud Storage) and optimise platform performance and cost at scale.
- Build and operationalise data governance, security, and compliance capabilities, including data contracts, access controls, and audit logging. Many of these practices are greenfield; you will be building them from scratch.
- Define and evolve team rituals, operating cadences, and engineering standards: code review norms, documentation practices, on-call fairness, and knowledge sharing.
- Represent the team to senior leadership and act as the single point of accountability for the team's commitments to cross-functional partners (analytics engineering, data analytics, product, finance).
- Own data source integrations and data quality across the platform, including third-party connectors (e.g. Fivetran), custom Scala/Spark ingestion pipelines, dbt orchestration via Airflow and Astronomer Cosmos, and quality monitoring and remediation practices.
Required Skills & Experience:
- 7+ years of experience in data platform engineering, data engineering, or distributed systems, including 2+ years leading engineering teams
- Strong programming skills in Scala and Python, including functional Scala patterns (cats-effect / Typelevel ecosystem), testing, code review, and production-grade engineering practices
- Deep hands-on experience with distributed computing frameworks: Apache Spark (Dataproc) and large-scale data processing. Databricks experience is useful for leading the migration off it
- Expert-level knowledge of cloud-native data platforms and services. Our stack runs on Google Cloud Platform (BigQuery, Dataproc, Cloud Storage), but equivalent depth on AWS or Azure is equally valued
- Advanced SQL skills including complex query optimisation, partitioning strategies, and performance tuning
- Experience building and scaling engineering teams, not just managing established ones: hiring, running interview loops, onboarding, and growing engineers from junior to senior
- Experience leading cross-team architecture initiatives and establishing platform-wide standards
- Experience with orchestration tools (Airflow, Astronomer, and Astronomer Cosmos for dbt orchestration) and CI/CD pipeline design
- Familiarity with legacy CI/CD tooling (e.g. Jenkins), particularly in the context of pipeline migration and modernisation
- Strong understanding of data governance, security best practices, and compliance requirements
- Experience with infrastructure as code and automated deployment practices
- Excellent communication skills, with the ability to translate technical trade-offs for non-technical stakeholders and represent the team to senior leadership
- Strong people management skills: coaching, mentorship, performance management, and career development
- Experience with monitoring, alerting, observability, and on-call operations for production data systems
Nice to Have:
- Experience in the digital marketing technology or partnership/affiliate industry
- General experience with Google Cloud Platform services beyond the core required stack (e.g. Pub/Sub, BigTable, Cloud Functions); familiarity with Dataflow is a plus but not a current platform requirement (it is under future evaluation)
- Familiarity with streaming technologies such as Apache Kafka and stream processing frameworks
- Experience standing up data quality frameworks and monitoring practices from scratch
- Experience with FinOps or cloud cost management at scale
- Experience migrating off legacy platforms (e.g. Databricks, on-prem Hadoop) to cloud-native alternatives
- Experience with organisational design, team scaling, or building engineering teams from scratch
- Awareness of AI agent architectures and how they connect to data platforms (e.g. MCP, tool-use patterns)
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)
Benefits:
- Hybrid, Casual work environment
- Responsible PTO policy
- Take the time off that you need. We are truly committed to a positive work-life balance, recognising that it is important to be happy and fulfilled in both
- Training & Development
- Learning the advanced partnership automation products
- Medical Aid and Provident Fund
- Group schemes with Discovery & Bonitas for medical aid
- Group scheme for provident fund
- Restricted Stock Units
- 3-year vesting schedule pending Board approval
- Internet Allowance
- Fitness club fee reimbursementsTechnology stipend
- Primary Caregiver Leave
- Mental Health and Wellness Benefit - Including 12 Therapy/Coaching sessions + Dependent coverage
impact.com is proud to be an equal opportunity workplace. All employees and applicants for employment shall be given fair treatment and equal employment opportunity regardless of their race, ethnicity or ancestry, color or caste, religion or belief, age, sex (including gender identity, gender reassignment, sexual orientation, pregnancy/maternity), national origin, weight, neurodivergence, disability, marital and civil partnership status, caregiving status, veteran status, genetic information, political affiliation, or other prohibited non-merit factors.