Staff Software Engineer - Data Platform

Responsibilities

• Own and operate core data infrastructure, including event streaming, relational database, and batch processing platforms.
• Design and implement highly reliable, observable, and scalable data systems that enable real-time and batch data processing.
• Develop automation and guardrails for data governance, retention, and compliance, ensuring auditability and consistency across services.
• Partner with application, platform, and SRE teams to improve data access patterns, reliability SLAs, and recovery processes.
• Establish standards for data infrastructure monitoring, alerting, and capacity planning, ensuring proactive issue detection.
• Drive operational excellence by improving resilience, reducing toil, and implementing self-healing or automated recovery mechanisms.
• Evolve and optimize data pipelines that support downstream analytics, identity verification, and machine learning systems.
• Evaluate, implement, and operate event-driven and batch data platforms such as Kafka, Google Pub/Sub, Dataflow, or Temporal.
• Lead incident response and root cause analysis for production data systems, contributing to postmortems and platform improvements.
• Mentor engineers and advocate for reliability-focused engineering culture across teams.
• Design and build the data lake storage and compute topology (object storage, partitioning, lifecycle, tiering) to support batch and streaming workloads.

Minimum Qualifications

• Bachelor's or Graduate degree in Computer Science, Software Engineering, or a related technical field.
• 8+ years of professional experience in data engineering, software engineering, or distributed systems development.
• 6+ years of programming experience in one or more languages such as Go, Python, or Java, with emphasis on automation and data system integration.

Preferred Qualifications

• Deep expertise in building and operating data systems—including relational databases, streaming, and batch platforms—in production environments.
• Hands-on experience administering and optimizing PostgreSQL or other relational databases in the cloud (AWS RDS, CloudSQL, or AlloyDB).
• Solid understanding of reliability engineering principles, including observability, SLOs, capacity management, and operational readiness.
• Experience managing cloud infrastructure (AWS or GCP) using infrastructure-as-code tools like Terraform, Kubernetes, or Helm.
• Experience operating event streaming platforms such as Kafka or Google Pub/Sub.
• Experience with batch and stream processing systems, including Dataflow, Temporal, or Airflow.
• Strong knowledge of data pipeline orchestration, change data capture, and schema management.
• Background in automation, incident response, and data platform observability.
• Familiarity with data governance and regulatory compliance frameworks (e.g., FedRAMP, GDPR, NIST).
• Contributions to open-source data infrastructure projects or strong community engagement in the data reliability space.
• Passion for performance engineering, system design, and mentoring others to deliver operational excellence at scale.
• AI-assisted development — Demonstrable experience using AI developer tools (e.g., code generation, test generation, query synthesis).
• Data-aware LLM usage — Ability to safely use large language models for tasks such as SQL generation, data lineage summarization, and runbook drafting.