Bangalore, Karnataka, India
Information Technology
Full-Time
MontyCloud
Overview
Role Overview
As Principal Data Engineer, you will drive the architecture and technical direction for MontyCloud’s next-generation data and knowledge platforms, enabling intelligent automation, advanced analytics, and AI-driven products for a wide range of users.
You will play a pivotal role in shaping the data foundation for AI-driven systems, ensuring our platform is robust, scalable, and ready to support state-of-the-art AI workflows. You will also lead the efforts in maintaining stringent data security standards, safeguarding sensitive information throughout data pipelines and platforms.
Key Responsibilities
Must Have
As Principal Data Engineer, you will drive the architecture and technical direction for MontyCloud’s next-generation data and knowledge platforms, enabling intelligent automation, advanced analytics, and AI-driven products for a wide range of users.
You will play a pivotal role in shaping the data foundation for AI-driven systems, ensuring our platform is robust, scalable, and ready to support state-of-the-art AI workflows. You will also lead the efforts in maintaining stringent data security standards, safeguarding sensitive information throughout data pipelines and platforms.
Key Responsibilities
- Architect and optimize scalable data platforms that support advanced analytics, AI/ML capabilities, and unified knowledge access.
- Lead the design and implementation of high-throughput data pipelines and data lakes for both batch and real-time workloads.
- Set technical standards for data modeling, data quality, metadata management, and lineage tracking, with a strong focus on AI-readiness.
- Design and implement secure, extensible data connectors and frameworks for integrating customer-provided data streams.
- Build robust systems for processing and contextualizing data, including reconstructing event timelines and enabling higher-order intelligence.
- Partner with data scientists, ML engineers, and cross-functional stakeholders to operationalize data for machine learning and AI-driven insights.
- Evaluate and adopt best-in-class tools from the modern AI data stack (e.g., feature stores, orchestration frameworks, vector databases, ML pipelines).
- Drive innovation and continuous improvement in data engineering practices, data governance, and automation.
- Provide mentorship and technical leadership to the broader engineering team.
- Champion security, compliance, and privacy best practices in multi-tenant, cloud-native environments.
Must Have
- Deep expertise in cloud-native data engineering (AWS preferred), including large-scale data lakes, warehouses, and event-driven/data streaming architectures.
- Hands-on experience building and maintaining data pipelines with modern frameworks (e.g., Spark, Kafka, Airflow, dbt).
- Strong track record of enabling AI/ML workflows, including data preparation, feature engineering, and ML pipeline operationalization.
- Familiarity with modern AI/ML data stack components such as feature stores (e.g., Feast), vector databases (e.g., Pinecone, Weaviate), orchestration tools (e.g., Airflow, Prefect), and ML ops tools (e.g., MLflow, Tecton).
- Experience working with modern open table formats such as Apache Iceberg, Delta Lake, or Hudi for scalable data lake and lakehouse architectures.
- Experience implementing data privacy frameworks such as GDPR and supporting data anonymization for diverse use cases.
- Strong understanding of data privacy, RBAC, encryption, and compliance in multi-tenant platforms.
- Experience with metadata management, semantic layers, or knowledge of graph architectures.
- Exposure to SaaS and multi-cloud environments serving both internal and external consumers.
- Background in supporting AI Agents or AI-driven automation in production environments.
- Experience processing high-volume cloud infrastructure telemetry, including AWS CloudTrail, CloudWatch logs, and other event-driven data sources, to support real-time monitoring, anomaly detection, and operational analytics.
- 10+ years of experience in data engineering, distributed systems, or related fields.
- Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (preferred).
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