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FloQastFloQastSan Jose, CA

Senior Staff Engineer, Data

Leads architecture and evolution of a large-scale data platform spanning ingestion, lakehouse storage, streaming, governance, and access. Requires 10+ years of software engineering experience, deep production expertise with Spark and distributed systems, and strong cloud data-platform experience.

Salary not listed
Hybrid7+ YOEData Engineering

About the role

Responsibilities

  • Lead the design of scalable and reliable data pipelines, storage solutions, and access layers using modern cloud-native technologies.
  • Own and evolve core data platform components, including event streaming, lakehouse architecture, batch and streaming ETL, and data cataloging.
  • Define best practices for data quality, lineage, privacy, and access control to ensure regulatory compliance and trust in the data.
  • Collaborate with Product, Analytics, Infrastructure, and Security teams to align data platform capabilities with organizational goals.
  • Mentor engineers across the organization, establish coding and architectural standards, and influence the strategic direction of the platform.
  • Evaluate and integrate technologies that improve performance, observability, developer experience, and cost efficiency.
  • Define compute architecture and standards for Spark jobs across the data platform.
  • Build and operate production-grade Spark pipelines using Apache Iceberg, Kafka, and cloud-native data services.

Requirements

  • 10+ years of software engineering experience with deep expertise in data infrastructure, distributed systems, or backend platform engineering.
  • Proven experience designing and delivering production-grade data platforms at scale, including platforms supporting hundreds of terabytes of data and thousands of jobs per day.
  • Strong experience with Snowflake, dbt, Kafka, Airflow, Spark, and cloud-native technologies such as AWS, GCP, or Azure.
  • Hands-on experience building APIs and services for data access, metadata management, and platform observability.
  • Familiarity with GDPR, SOC 2, HIPAA, and enterprise-grade security practices.
  • Effective communication and collaboration skills, with the ability to influence engineering and non-engineering audiences.
  • Experience in a startup or high-growth SaaS environment.

Nice-to-Haves

  • Exposure to AI/ML data pipelines or real-time analytics platforms.
  • Production experience with Apache Iceberg, Structured Streaming, Kafka on MSK, AWS S3, AWS Glue, MWAA, Trino, or Athena.
  • Expertise in Spark performance tuning, including shuffle behavior, partition strategies, memory pressure, Catalyst optimization, and Spark UI analysis.
  • Experience choosing between PySpark and Scala Spark for production workloads.

Compensation and Benefits

  • The posting does not specify compensation or benefits.

Skills

Sparkapache icebergKafkaAirflowSnowflakedbtAWSaws s3aws gluemwaatrinoathenapysparkScalastructured streaming

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