Data Engineer
About the Role
As a Data Engineer II within our Engineering Platform team, you will own the full lifecycle of data ingestion pipelines that fuel Kpler’s core intelligence platforms, including Snowflake, ClickHouse, and direct client delivery channels. Operating with high autonomy and operational rigor, you will build and maintain high-throughput ingestion frameworks from sources like Kafka, S3, and Iceberg while ensuring exceptional reliability, performance, and observability. In this role, you will collaborate closely with cross-functional engineering crews, cloud infrastructure teams, and external technical teams at clients to deliver seamless, scalable data flows across our global product ecosystem.
Key Responsibilities
Architect and maintain data ingestion pipelines: Design, implement, and operate flexible data ingestion frameworks using Java (sourcing from Kafka, S3, and Iceberg) and Python with Airflow to power analytical platforms like Snowflake and ClickHouse.
Drive operational excellence and reliability: Maintain end-to-end service ownership by setting up proactive monitoring, establishing actionable alerts, reducing pipeline failure modes, and participating in on-call readiness.
Lead technical project delivery: Break down complex engineering problems, author clear design proposals, and lead peer engineers to ship capabilities end-to-end on schedule.
Elevate developer experience and platform standards: Build reusable tools, clear documentation, and paved paths that enable adjacent engineering crews to adopt and extend data ingestion frameworks with minimal friction.
Partner across engineering and client stakeholders: Collaborate with Cloud Platform engineers, Product Managers, tech leads, and technical teams at clients to align pipeline capabilities with broader business and customer needs.
Mentor and grow the engineering crew: Onboard new engineers, conduct constructive code reviews, and share technical best practices to foster continuous improvement across the team.
Experience & Background
Data ingestion & engineering experience: ~3+ years of professional software or data engineering experience building, operating, and maintaining production data ingestion pipelines (batch/streaming) using tools like Kafka, S3, Iceberg, or Airflow.
Core programming & data stack depth: High proficiency in Java and Python, backed by strong SQL skills, data modeling fundamentals, and analytical warehouse experience (Snowflake or equivalent cloud data warehouse).
Production & DevOps mindset: Demonstrated ownership of production cloud services on AWS, including monitoring/observability, performance tuning, and Kubernetes deployment workflows (GitOps/CI/CD).
Collaborative communication: Strong communication skills to partner with cross-functional product, platform, and external client technical teams.
Snowflake & ClickHouse optimization: Hands-on experience optimizing Snowflake resource sizing and cost/performance trade-offs, or operating OLAP engines like ClickHouse.
Client delivery patterns: Familiarity with AWS Transfer Family (sFTP), Excel add-in integrations, or Python boto3 client data delivery setups.
Frontend awareness: Basic exposure to TypeScript, Vue.js, or API integrations for presenting data in end-user workflows.