Principal Engineer - Data Engineering at Freshworks – Hyderabad | Jobyssey

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.

At Freshworks, we have fostered an environment that enables everyone to find their true potential, purpose, and passion, welcoming colleagues of all backgrounds, genders, sexual orientations, religions, and ethnicities. We are committed to providing equal opportunity and believe that diversity in the workplace creates a more vibrant, richer environment that boosts the goals of our employees, communities, and business. Fresh vision. Real impact. Come build it with us.

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