Senior Data Platform Engineer

Job Description

Drive end-to-end data engineering and data platform initiatives, from problem discovery and technical design to implementation, rollout, documentation, and long-term ownership. Design, build, and operate reliable, scalable, and maintainable data pipelines that support analytics, product insights, experimentation, and business-critical reporting. Work with large-scale batch and streaming data processing systems, ensuring data is accurate, timely, observable, and easy to consume. Collaborate closely with engineering, product, analytics, data science, and business stakeholders to understand needs, clarify requirements, and translate them into pragmatic technical solutions. Improve the reliability, performance, and operational maturity of the data platform, including monitoring, alerting, data quality checks, failure handling, and incident response. Contribute to the evolution of our data architecture, including data modeling, warehouse/lakehouse practices, schema design, data contracts, ingestion patterns, and platform standards. Help identify gaps in our current data ecosystem and propose scalable solutions that reduce complexity, improve developer experience, and enable teams to move faster. Lead technical discussions and contribute to design reviews, architecture decisions, and roadmap planning for data platform initiatives. Mentor and support other engineers through code reviews, pairing, technical guidance, and knowledge sharing. Promote strong engineering practices across the team, including testing, documentation, observability, maintainability, and thoughtful trade-off analysis. Take ownership of ambiguous or cross-functional problems and help turn them into clear, actionable initiatives with measurable impact.

Skills Required

• Scala
• Java
• Python
• Software Engineering
• Distributed Systems
• Data Pipelines
• Batch Processing
• Streaming Data
• Apache Spark
• Databases
• SQL
• Query Optimization
• Data Modeling
• Data Warehousing
• Data Lakehouse
• Large Datasets
• Monitoring
• Alerting
• Data Quality
• Incident Response
• Data Architecture
• Schema Design
• Mentoring
• Communication
• Ownership Mindset
• Kubernetes
• Containerized Workloads
• Workflow Orchestration
• CI/CD
• Infrastructure-as-Code
• Cloud-native Data Platform Tooling
• Kafka
• Flink
• Spark Structured Streaming
• Observability Tooling
• Internal Platforms
• Developer Tooling
• Self-service Data Products
• ClickHouse
• Trino
• Iceberg
• Delta Lake
• dbt
• Airflow
• Argo Workflows