Team Lead Analytics Data Platform
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 senior Team Leader to drive the architecture, reliability, and evolution of impact.com's Analytics Data Platform. As Team Lead for Data Platform Engineering, you'll provide hands-on technical leadership across platform subsystems. You'll design scalable infrastructure, lead strategic initiatives, shape engineering standards, and mentor engineers across levels. This is a systems and platform engineering role focused on distributed data processing atterabyte scale. You'll work across the full data ecosystem (ingestion, processing, storage, orchestration, access, and governance) using technologies like Scala, Python, Google Cloud Dataproc, Databricks, BigQuery, and Airflow. Our ideal candidate combines deep distributed systems expertise with the ability to lead architecture across teams and translate platform trade-offs into business language. This role is for someone who wants to define how a data platform scales, not just maintain what exists.You'll join a platform with strong foundations: Scala/Spark pipelines, multiple Fivetran connectors, Airflow on Astronomer, and a mature dbt-based transformation layer. But there are significant opportunities to improve reliability practices, data quality, governance, observability, and cost management.
What you'll do:
- 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 and Databricks) processing TB-scale data.
- Lead strategic technical initiatives that modernise or significantly extend the Analytics Data Platform's capabilities.
- Establish platform reliability practices: define SLOs/SLAs, build incident management and response processes, and drive systemic improvements across pipeline and infrastructure operations.
- Optimise platform performance and cost at scale, including benchmarking, capacity planning, query tuning, and cloud cost strategy.
- Design and maintain core platform services: Dataproc clusters, Databricks workspaces, BigQuery datasets, Cloud Storage, and associated GCP infrastructure.
- 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, not inheriting mature systems.
- Mentor engineers across levels on platform technologies, distributed systems, and engineering best practices.
- Partner with analytics engineers, data analysts, and stakeholders to ensure the platform meets current and future needs.
- 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.
- Evaluate and adopt emerging integration standards (e.g. Model Context Protocol) and agentic AI patterns to enable secure, governed AI access to platform data sources and services.
Required Skills & Experience:
- 7+ years of experience in data platform engineering, data engineering, or distributed systems
- 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, Databricks) and large-scale data processing
- 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
- Proven 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
- Demonstrated ability to mentor engineers and lead technical design reviews
- Excellent communication skills, with the ability to translate technical trade-offs for non-technical stakeholders
- 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
- Awareness of AI agent architectures and integration protocols (e.g. MCP, tool-use patterns, LLM-to-data-source connectivity) and the ability to assess their applicability to platform design
- Experience designing platform APIs or service layers that can be consumed by both human users and autonomous AI agents, with appropriate access controls and observability
- 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.