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.