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Partner with sales and engineering teams to design and deliver Databricks-based big data and ML solutions for strategic customers. Drive technical discovery, build proofs-of-concept, validate cloud integrations, promote open-source projects (Spark, Delta Lake, MLflow), and build executive-level relationships to accelerate adoption and win competitive deals.

Design and build scalable backend infrastructure and services for the Databricks platform, including distributed systems, cloud storage integrations (S3, Azure Blob), data pipelines (Spark), and developer tooling. Deliver high-performance, reliable services and client libraries, and collaborate on SaaS and service-oriented architecture to support large-scale platform usage.

The Business Value Consultant partners with sales teams to drive financial impact in Financial Services, using data-driven solutions to address challenges and teach value consulting principles.

Sell Databricks enterprise cloud data platform into telco accounts, close net-new logos and existing accounts, manage complex sales cycles, meet revenue and activity targets, track opportunities in Salesforce, and build internal and external success plans.

Lead engineering teams to build and operate large-scale cloud services and developer platforms. Drive technical strategy, manage technical debt, ensure delivery, partner with product and customers, and grow engineering leaders while integrating AI into developer workflows.

Provide high-touch technical support for strategic Databricks customers: manage and prioritize critical issues, coordinate across Engineering/Product/Accounts, drive ticket closure, perform trend analysis, chair customer roundtables, and promote customer self-sufficiency.

Lead and grow a backend engineering team building scalable, cloud-agnostic infrastructure and services for Databricks. Hire and develop engineers, ensure engineering excellence (architecture reviews, testing, monitoring), drive roadmaps with product leadership, and coordinate cross-team execution to deliver reliable distributed systems and ML infrastructure.

Design, build, and maintain high-performance Databricks SQL connectors and drivers; improve reliability, telemetry, and Partner Connect integrations; engage open-source communities and collaborate cross-functionally to deliver scalable solutions.

Build and maintain scalable backend services and infrastructure for Databricks platform. Work on distributed systems, storage integrations (S3/Azure Blob), SaaS/service-oriented architectures, data pipelines (Spark), monitoring, and high-performance client libraries to support large-scale workloads.

Lead search quality for both LLM agents and human users by building and optimizing hybrid retrieval, ranking models, and evaluation frameworks across structured and unstructured data to ensure high-precision, high-recall results.