Data & Analytics Solutions Architect-6

Role overview

Architecture and platform responsibilities

• Engage stakeholders to define data strategy, platform requirements, architecture goals, and transformation priorities.
• Design end-to-end open-source data architectures and lead modernization across data lakes, lakehouses, distributed processing, and enterprise data ecosystems.
• Design ingestion, integration, streaming, storage, and consumption patterns using Spark, Kafka, Flink, NiFi, and Trino.
• Develop lakehouse solutions using Iceberg, Hudi, Delta Lake, object storage, and open table formats.
• Establish governance, security, metadata, lineage, privacy, and observability standards; evaluate technologies and define reusable architecture and DataOps practices.
• Provide technical leadership across data modeling, engineering, implementation, migration, testing, deployment, and production readiness.
• Optimize performance, capacity, reliability, recovery, and operations; conduct architecture reviews and produce HLD/LLD documents.
• Support solution design, RFPs, estimation, presentations, client workshops, use-case assessment, and business KPI definition.
• Integrate data, ML, and GenAI architectures; support AI/ML and LLM deployment on Kubernetes and establish MLOps/LLMOps and AI monitoring practices.
• Mentor architects and engineers, assist technical assessments and hiring, and develop reusable accelerators and engineering practices.

Required experience and expertise

• 14+ years in enterprise data architecture, open-source data platforms, distributed data processing, and data engineering.
• Experience designing open-source enterprise solutions across the data-to-insights value chain and leading large-scale platform modernization and cloud migration.
• Experience with architecture definition, HLD/LLD, NFR assessment, migration planning, technical governance, and production readiness.
• Hands-on expertise in Apache Spark, PySpark, Kafka, Flink, Airflow, NiFi, and Trino.
• Strong programming and query skills in Python, Scala, Java, and SQL.
• Experience with Apache Iceberg, Hudi, Delta Lake, and object storage.

Technical scope

• The role covers enterprise data architecture, open-source platforms, distributed data processing, lakehouse design, data engineering, and enterprise governance.
• Workloads include batch and real-time streaming, analytics, AI/ML, GenAI, and LLMs.
• Architecture practices include CI/CD, DataOps, MLOps, LLMOps, observability, metadata management, lineage, privacy, and security.