Freshworks
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Hyderabad · On-site · 8-11 Yrs · 5 days ago
Enterprise Software & Network Solutions · 5001 to 10000 Employees · San Mateo, US
Freshworks (NASDAQ: FRSH) provides AI-powered, unified service operations platform that is fast to deploy, intuitive to use, and enables every employee to be more productive. We give agile enterprises the depth they need to operate at scale without the operational drag of legacy platforms. Domain-specific AI built for IT, HR, finance and every business team on a decade of real service context.
What employees say
+ Above average work life balance
− Slow decision making process due to global teams
More reviews on the company profile.
Interview experience · Account Executive
The interview process consisted of five rounds. They expect candidates to have a thorough understanding of the product along with strong analytical skills. The biggest focus was product knowledge. They wanted to see how
Difficult difficulty · Chennai · 612 reports
More interview insightsJob description
Overview
Organizations everywhere struggle under the crushing costs and complexities of “solutions” that promise to simplify their lives.
Responsibilities
- 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.
- 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.
Qualifications
- 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.
- 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.
- Track record of defining technical direction and delivering organization-wide platform transformations.
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Job application FAQ
Last updated 15 September 2026
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