Principal Engineer - Data Engineering

About Freshworks:

Organizations everywhere struggle under the crushing costs and complexities of “solutions” that promise to simplify their lives. To create a better experience for their customers and employees. To help them grow. Software is a choice that can make or break a business. Create better or worse experiences. Propel or throttle growth. Business software has become a blocker instead of ways to get work done.

There’s another option. Freshworks. With a fresh vision for how the world works.

Freshworks Inc. builds uncomplicated service software that delivers exceptional employee and customer experiences. Our people-first approach to AI eliminates friction, helping businesses reduce complexity, lower cost-to-serve, and deliver faster, more human support through enterprise-grade yet easy-to-use CX and IT solutions. Nearly 75,000 companies, including Bridgestone, New Balance, Nucor, S&P Global, and Sony Music, trust Freshworks to power their Employee Experience (EX) and Customer Experience (CX) operations.

Fresh vision. Real impact. Come build it with us.

As the most senior individual contributor within our data engineering organization, the Principal Staff Engineer – Data will define the long-term technical vision for Freshworks' data platform. This strategic leader will orchestrate architectural decisions across data ingestion, processing, storage, governance, analytics, and AI/ML enablement to fuel global enterprise scale.

Impact You Can Create

Architect the Future Platform: Define and own the multi-year architectural vision and roadmap for Freshworks' enterprise data platform, aligning engineering capabilities with core business goals.

Scale Global Data Ingestion: Design and optimize real-time streaming and high-volume batch data platforms engineered to process complex workloads with ultra-low latency.

Accelerate AI/ML & GenAI Initiatives: Build the foundational, high-fidelity data capabilities, feature stores, and training-data pipelines that empower predictive AI and Generative AI frameworks.

Establish Universal Data Trust: Turn raw information into secure, discoverable, and reusable corporate data products by introducing enterprise-grade data governance, quality, and lineage standardizations.

Act as the Ultimate Technical Authority: Drive alignment across engineering, product, and executive stakeholders while raising the performance bar by mentoring Staff and Senior engineers.

Roles & Responsibilities

Strategic Technology Direction: Lead critical technology selections, macro architectural reviews, build-versus-buy evaluations, cloud migrations, and platform deprecation cycles.

Large-Scale Data Engineering: Design robust event-ingestion architectures, Change Data Capture (CDC) systems, and real-time streams using Kafka, Kinesis, or Pub/Sub.

Distributed Engine Processing: Lead the design and implementation of Spark-based distributed processing systems to handle massive, multi-tenant datasets efficiently.

Warehouse & Lakehouse Optimization: Build high-performance, cost-effective data serving layers using Snowflake and modern lakehouse architectures like Apache Iceberg, Delta Lake, and Databricks.

Platform Governance & Telemetry: Establish best practices for platform reliability, deep observability, system scalability, and FinOps-driven cost optimization strategies.

Data Productization & Semantic Modeling: Define reusable data models, structured semantic layers, and curated data products that support organization-wide self-service analytics.

Security, Privacy, & Governance: Champion enterprise standards for metadata management, automated data cataloging, rigorous data quality metrics, and compliance with global regulatory frameworks.

Skills

Distributed Systems Architecture: Masterful understanding of distributed computing principles, cloud-native integration patterns, and massive-scale multi-tenant data platform design.

Data Stack Expertise: Deep, hands-on command over Snowflake, Apache Spark, and…