Senior Software Engineer, Data

As a Senior Software Engineer, Data, you will be a key contributor driving the design, implementation, and optimization of FloQast’s core data platform. You will build and scale the critical systems that power data ingestion, storage, processing, and access across our product and analytics pipelines. Working closely with cross-functional teams, you will ensure our data infrastructure remains highly reliable, scalable, and secure, enabling engineering and business operations to build on a solid data foundation.

Data Platform Development: Contribute to the design and implementation of scalable, reliable data pipelines, storage solutions, and efficient access layers using modern cloud-native technologies.

Pipeline Execution: Build, optimize, and maintain core data platform components, including event streaming infrastructure, lakehouse storage layers, and both batch and streaming ETL workflows.

Data Quality & Security: Implement best practices for data quality, data lineage, and access controls to ensure robust security, regulatory compliance, and overall trust in our data assets.

Cross-functional Collaboration: Work closely with Product, Analytics, Infrastructure, and Security teams to deliver data capabilities that support team-specific and organizational goals.

Team Mentorship: Provide technical guidance and code reviews for other engineers on the team, helping to champion clean code, consistent engineering standards, and platform stability.

Performance & Observability: Evaluate, test, and integrate new tools and technologies that actively improve data pipeline performance, systems observability, and overall cost efficiency.

7+ years of software engineering experience with a strong background in data infrastructure, distributed systems, or backend data platform engineering.

Proven experience building and maintaining production-grade data pipelines at scale (e.g., handling large data volumes, optimizing job execution, and ensuring data reliability).

Strong hands-on experience with modern data ecosystem tools, such as Snowflake, dbt, Kafka, Airflow, Spark, and cloud-native services (e.g., AWS, GCP, or Azure).

Experience building and integrating APIs and services for data ingestion, access, and pipeline observability.

Good understanding of secure data handling, including familiarity with enterprise-grade security practices and data privacy considerations (such as SOC2 or GDPR).

Strong communication and collaboration skills, with a track record of partnering successfully across engineering and analytics teams.

Experience working within a startup or high-growth SaaS environment is highly preferred.

Exposure to or interest in AI/ML data pipelines or real-time analytics architectures.