Senior Data Architect & Engineering Lead

As a Senior Data Architect & Engineering Lead, you will play a crucial role in leading end-to-end data architecture across lakehouse/warehouse ecosystems and driving the engineering roadmap. Your responsibilities will include owning standards, governance, scalability, and cost efficiency while mentoring teams, partnering with product and platform leaders, and ensuring that data enables advanced analytics and AI initiatives. - Architecture Ownership: Define and evolve domain-driven, event-driven, and lakehouse architectures such as Delta, Apache Iceberg, and Hudi. - Platform Strategy: Select and implement platform components including streaming, storage, warehouse, orchestration, catalog, and observability; guide buy-vs-build decisions. - Scalable Engineering: Lead the design of batch/streaming pipelines, CDC, change-data capture, and real-time serving layers to ensure efficient data processing. - Data Governance: Establish data standards, access models, PII redaction, retention policies, and audit mechanisms while championing data contracts. - Cost & Performance Optimization: Optimize compute/storage sizes, implement workload management, caching strategies, and partitioning/clustering techniques. - ML/AI Readiness: Enable feature stores, model-ready datasets, and MLOps integrations for reproducible data practices. - Leadership & Mentoring: Coach engineers, conduct design reviews, and nurture a culture of quality and automation among the team. - Stakeholder Management: Translate business strategy into data roadmaps, drive cross-functional programs, and oversee migrations effectively. - Risk & Security: Take ownership of compliance posture, encryption, secrets management, key rotation, and incident playbooks for data security. - Documentation & Enablement: Develop reference architectures, blueprints, reusable templates, and provide internal training to enhance data understanding and usage. Required Skills: - Deep expertise in data modeling, distributed systems, warehouse/lakehouse technologies like Snowflake, BigQuery, Redshift, Delta, and Iceberg. - Proficiency in Advanced SQL, Python/Scala, Spark, stream processing using platforms like Kafka, Flink, and Spark Structured Streaming. - Experience with orchestration tools such as Airflow, Prefect, CI/CD, IaC (Terraform), and containerization with Docker/Kubernetes. - Strong understanding of security, governance, data privacy, lineage, cataloging, and observability. - Architectural leadership abilities demonstrated through ADRs, RFCs, cost modeling, and vendor evaluation. Preferred Qualifications: - Exposure to microservices data patterns, Data Mesh, event sourcing, and CQRS. - Hands-on experience with feature stores like Feast, Tecton, ML pipelines such as Kubeflow, Vertex, Azure ML, and MLOps practices. - Familiarity with genAI data considerations including vector stores, embeddings, and retrieval pipelines. - Previous leadership involvement in multi-cloud or large migration programs. - Azure/AWS certifications would be advantageous. Please note that the job location is New Delhi/Bangalore (Hybrid), with an experience requirement of 7-10 years, a notice period of 30 days, and a salary of 30 LPA. As a Senior Data Architect & Engineering Lead, you will play a crucial role in leading end-to-end data architecture across lakehouse/warehouse ecosystems and driving the engineering roadmap. Your responsibilities will include owning standards, governance, scalability, and cost efficiency while mentoring teams, partnering with product and platform leaders, and ensuring that data enables advanced analytics and AI initiatives. - Architecture Ownership: Define and evolve domain-driven, event-driven, and lakehouse architectures such as Delta, Apache Iceberg, and Hudi. - Platform Strategy: Select and implement platform components including streaming, storage, warehouse, orchestration, catalog, and observability; guide buy-vs-build decisions. - Scalable Engineering: Lead the design of batch/streaming pipelines, CDC, change-data capture, and real-time serving layers to ensure efficient data processing. - Data Governance: Establish data standards, access models, PII redaction, retention policies, and audit mechanisms while championing data contracts. - Cost & Performance Optimization: Optimize compute/storage sizes, implement workload management, caching strategies, and partitioning/clustering techniques. - ML/AI Readiness: Enable feature stores, model-ready datasets, and MLOps integrations for reproducible data practices. - Leadership & Mentoring: Coach engineers, conduct design reviews, and nurture a culture of quality and automation among the team. - Stakeholder Management: Translate business strategy into data roadmaps, drive cross-functional programs, and oversee migrations effectively. - Risk & Security: Take ownership of compliance posture, encryption, secrets management, key rotatio