Resident Solution Architect

Accountabilities:: Act as a trusted technical advisor to enterprise customers, understanding their business objectives and translating them into practical data and AI solutions. Gather and analyze customer technical requirements and design scalable, secure, and maintainable data architectures. Design and implement modern Lakehouse and Data + AI architectures using Databricks and associated platform capabilities. Develop and guide proof-of-concepts, technical workshops, architecture demonstrations, and solution evaluations. Work extensively with Apache Spark, PySpark, SQL, and Databricks to build and optimize data engineering solutions. Design scalable data ingestion, transformation, processing, and analytics pipelines. Leverage technologies such as Delta Lake and Unity Catalog to support reliable, governed, and scalable data platforms. Support data migration, modernization, and cloud transformation initiatives. Integrate data platforms with AWS, Azure, or GCP and provide guidance on cloud architecture and deployment strategies. Troubleshoot performance, scalability, reliability, and data pipeline challenges across customer environments. Collaborate with customer engineering, data science, architecture, and leadership teams to drive successful technical outcomes. Provide technical recommendations across data engineering, analytics, machine learning, AI, governance, and cloud architecture. Create reference architectures, technical documentation, implementation guidance, and other reusable technical assets. Support production deployments and help customers successfully adopt and scale data platform solutions. Conduct enablement sessions, knowledge-sharing activities, and technical presentations for customer and internal teams. Requirements: 9+ years of professional experience in data engineering, software engineering, solution architecture, or closely related technical disciplines. Strong hands-on expertise with Databricks, Apache Spark/PySpark, SQL, and modern data engineering practices. Proven experience designing and implementing data pipelines, data platforms, Lakehouse architectures, data warehouses, or large-scale data processing environments. Strong understanding of data engineering fundamentals, including ingestion, transformation, processing, storage, data quality, scalability, and reliability. Experience with at least one major cloud platform: AWS, Azure, or GCP. Demonstrated solution architecture and technical design experience, with the ability to translate complex customer requirements into scalable technical solutions. Strong customer-facing and consulting experience, ideally working directly with enterprise engineering, data, architecture, and leadership stakeholders. Excellent communication and presentation skills, with the ability to lead workshops, explain complex technical concepts, and influence technical decisions. Strong Python skills and practical experience applying programming to data engineering or analytics solutions. Experience with Delta Lake, Unity Catalog, MLflow, Kafka, streaming, data migration, MLOps, machine learning, GenAI, data governance, Terraform, CI/CD, Snowflake, or the Hadoop ecosystem is advantageous. Strong troubleshooting and problem-solving skills, particularly around data pipelines, performance, scalability, and production environments. Ability to work independently in a remote environment while managing multiple customer engagements and technical priorities. Benefits: Fully remote position for candidates based in India. Full-time employment with an annual compensation range of ₹35,00,000–₹60,00,000 , depending on experience and skills. Opportunity to work directly with enterprise customers on complex data, AI, and cloud transformation initiatives. Hands-on exposure to Databricks, Apache Spark, Lakehouse architecture, cloud data platforms, and modern Data + AI technologies. Significant involvement across the full solution lifecycle, from discovery and POCs through production deployment and adoption. Opportunities to lead technical workshops, architecture engagements, enablement sessions, and customer-facing initiatives. Exposure to AWS, Azure, and GCP environments and a broad range of modern data technologies. Opportunity to influence architecture decisions and provide strategic technical guidance to enterprise customers. Remote-first working environment with flexibility and autonomy. Professional growth through challenging architecture, consulting, data engineering, and AI projects.