Principal Engineer - Data Engineering

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 cloud data ecosystems (AWS, GCP, or Azure). Streaming & Storage Paradigms: Expert knowledge of real-time ingestion mechanics (Kafka, Kinesis, CDC) and lakehouse technologies (Iceberg, Delta Lake, Databricks). Analytics & Data Modeling: Advanced competency in database schema design, semantic layer configuration, and data virtualization patterns. FinOps & Observability: Proven capability to optimize compute costs and implement advanced infrastructure monitoring and lineage tracing solutions. Stakeholder Architecture: Elite communication, presentation, and negotiation skills, with a natural ability to translate intricate technical realities into clear business strategies for executive leaders. Qualifications Professional Timeline: 15+ years of progressive individual contributor experience in Data Engineering, Data Platform Engineering, or Data Architecture. Transformation Track Record: A verifiable history of designing and operating large-scale, live production data environments and delivering organization-wide platform transformations. Industry Domain: Proven success leading cross-functional architecture initiatives within SaaS, cloud-native, or fast-paced product engineering organizations. Talent Leadership: Demonstrated experience driving technical strategy and successfully mentoring Staff-level and Senior engineering talent. Education Baseline: Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related quantitative technical discipline. Preferred Qualifications Experience with lakehouse technologies such as Delta Lake, Apache Iceberg, and Databricks. Experience building data platforms supporting AI/ML, GenAI, feature stores, or training-data pipelines. Experience with enterprise data governance, metadata management, cataloging, lineage, and observability platforms. Experience driving platform cost optimization and FinOps initiatives for large-scale data environments. Track record of defining technical direction and delivering organization-wide platform transformations. 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.