Solution Architect

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Resident Solution Architect (RSA) – Data & Databricks based in India.

• Design end-to-end data, analytics, and Lakehouse solutions using Databricks. Translate business objectives and technical requirements into scalable architectures that support enterprise data processing, analytics, and reporting needs.
• Develop, review, and optimize robust ETL/ELT pipelines and enterprise data platforms using Databricks, Apache Spark, PySpark, Python, and SQL. Establish best practices for data transformation, modeling, warehousing, and distributed data processing.
• Design and implement solutions using Delta Lake, Unity Catalog, Databricks SQL, and Databricks Workflows. Ensure data platforms are secure, reliable, maintainable, and aligned with enterprise architecture standards.
• Architect scalable data environments across Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP). Support cloud data platform modernization, migration, and transformation initiatives while optimizing infrastructure utilization and operational efficiency.
• Lead technical discovery sessions, architecture workshops, and solution discussions with enterprise customers. Engage with architects, CTOs, CDOs, engineering managers, and other senior stakeholders to understand requirements, present recommendations, and communicate architectural decisions, risks, and trade-offs.
• Prepare high-level designs (HLDs), low-level designs (LLDs), architecture diagrams, technical proposals, and implementation documentation. Establish architectural guidelines and review solution designs to ensure consistency, scalability, and long-term maintainability.
• Lead proofs of concept (POCs), technical demonstrations, architecture assessments, and solution validation activities. Evaluate alternative approaches, validate technical feasibility, and demonstrate how proposed solutions address customer requirements.
• Diagnose and resolve complex issues affecting Databricks and Spark workloads. Optimize data processing performance, resource utilization, reliability, scalability, and cloud costs while identifying opportunities for continuous improvement.
• Provide architectural direction, technical guidance, and mentorship to data engineering teams. Review code, data pipelines, and implementation approaches to promote engineering excellence and ensure alignment with architectural standards.
• Partner with sales, pre-sales, delivery, product, and engineering teams to develop effective technical solutions. Contribute to solution positioning, technical feasibility assessments, and successful customer engagements throughout the solution lifecycle.
• Identify opportunities to improve data architecture, automation, development practices, and operational efficiency. Stay informed about advancements in Databricks, cloud data platforms, distributed computing, and modern data engineering technologies.

• At least 8 years of experience in data engineering, data architecture, solution architecture, big data, or a related technical field, with demonstrated experience designing and delivering enterprise-grade data solutions.
• Strong hands-on experience with Databricks and enterprise data platforms, including Lakehouse architecture, Delta Lake, Unity Catalog, Databricks SQL, and Databricks Workflows.
• Deep knowledge of Apache Spark and PySpark, including distributed processing concepts, data transformation strategies, and the design of scalable data pipelines.
• Advanced proficiency in Python and SQL, with experience developing, maintaining, reviewing, and optimizing production-grade data engineering solutions.
• Proven experience designing scalable ETL/ELT pipelines, data models, data warehouses, and enterprise data platforms. Strong understanding of data architecture principles, data processing patterns, reliability, scalability, and maintainability.
• Practical experience with at least one major cloud provider: Azure, AWS, or GCP. Ability to design and evaluate cloud-native data architectures and make informed decisions regarding performance, reliability, and cost.
• Hands-on experience troubleshooting and optimizing Databricks and Spark workloads.