Were looking for a hands-on Data Platform Team Lead to guide a high-performing team in designing, scaling, and operating the infrastructure that powers Chargeflows platform.
This role combines architectural leadership with people management. Youll define the technical direction for our data platform while developing engineers, strengthening execution, and building a culture of ownership and technical excellence.
Youll be responsible for the reliability, performance, security, and cost efficiency of data systems supporting millions of daily transactions and critical product, machine-learning, and analytics use cases. This is a role for someone who can move comfortably between long-term architecture, hands-on technical problem-solving, and day-to-day team leadership.
The impact youll have
Build and evolve the data infrastructure that safeguards our clients revenue and powers critical product, ML, and analytics use cases.
Establish scalable engineering standards and operational practices (reliability, security, governance, and cost efficiency).
Lead and grow a team of data/infrastructure engineers while cultivating technical excellence and psychological safety.
What youll own (Your arena)
Leadership & Mentorship: Lead, mentor, and scale a team of talented data and infrastructure engineers.
Create strong execution rituals: clear ownership, high-quality delivery, feedback loops, and growth plans.
Data Infrastructure & FinOps: Own the architecture and evolution of our modern data stack and platform services.
Drive cloud cost management (FinOps): cost visibility, optimization initiatives, and capacity planning.
High-Performance Engineering: Oversee the design of distributed systems, real-time streaming, and batch pipelines capable of processing millions of daily transactions with minimal latency.
Define technical direction for storage, compute, and data movement patterns (event-driven + lakehouse).
Operational Excellence Champion Infrastructure-as-Code (IaC), CI/CD, and testing practices for infrastructure and data systems.
Establish observability standards (monitoring/alerting, SLOs, incident response, postmortems).
Partner on security compliance and data governance across the R&D organization.
Strategic Collaboration: Act as the bridge between data engineering, product, data science, and core R&D teams to execute cross-functional initiatives.
Translate business goals into platform roadmaps, milestones, and measurable outcomes.
Requirements: What You Bring
Experience: 6+ years in data platform engineering, data infrastructure, or backend engineering, including 2+ years leading/managing/mentoring engineers.
Strong software engineering: Proficiency in Python (or similar) and modern practices (clean code, CI/CD, testing paradigms).
Data architecture: Extensive experience architecting and operating scalable data lakes/lakehouses, distributed systems, and real-time event-driven architectures.
Cloud-native & IaC: Experience with AWS, GCP, or Azure, plus Docker/Kubernetes and Infrastructure-as-Code (e.g., Terraform).
Databases & storage: Strong knowledge of relational (e.g., PostgreSQL), NoSQL, and analytical databases, including performance optimization, schema design, and cost tuning at scale.
Execution & delivery: Proven track record managing sprint planning, scoping technical projects, and delivering complex infrastructure roadmaps on time.
Nice to Have
Advanced data stack: Apache Iceberg (Lakehouse/S3/Glue), Apache Spark optimization, and big data processing.
Streaming & messaging: Kafka, Kinesis, Flink, Kafka Streams.
Modern tooling: Temporal/Dagster, dbt/DuckDB, Datadog/Grafana, Pydantic.
FinOps & governance: Spot instances/savings plans and compliance frameworks (GDPR/PCI-DSS).
This position is open to all candidates.