Designs, builds, and deploys production-grade data engineering, analytics, GenAI, and agentic AI solutions for enterprise clients on Databricks. Responsibilities include Lakehouse architecture, data pipeline development, AI/ML workflows, RAG and natural-language analytics solutions, cloud migration, legacy BI modernization, performance optimization, technical workshops, consultative discovery, and cross-functional leadership with clients and Databricks teams.
Tiger Analytics is looking for experienced Forward Deployed Engineer with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.
We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world.
Role Overview:
We're looking for a Forward Deployed Engineer who thrives at the intersection of client problem-solving and hands-on AI engineering. You'll partner directly with clients and engineering teams to turn complex business challenges into production-grade GenAI and Agentic AI solutions — owning the journey from first conversation to live deployment.
Role :
We are seeking an experienced, client-facing Forward Deployed Engineer (FDE) with deep expertise in the Databricks Data Intelligence Platform to partner closely with enterprise clients and Databricks account teams. In this role, you will embed directly into customer environments to architect, build, and deploy high-impact data engineering, analytics, and AI/GenAI solutions—turning complex, ambiguous business problems into production-ready outcomes at speed
Responsibilities:
- Rapid Solution Prototyping & Delivery: Embed with client engineering and business stakeholders to design, build, and deploy end-to-end production solutions on Databricks within accelerated timelines.
- Lakehouse Architecture & Data Engineering: Architect scalable data pipelines using Delta Lake, Unity Catalog, and Apache Spark, handling complex cloud migrations, legacy BI modernization, and performance/cost optimization.
- AI, GenAI & Intelligent Analytics: Build and deploy enterprise AI/ML workflows, knowledge layers/ontologies, and natural-language analytics solutions using Databricks AI/BI Genie, Mosaic AI, RAG architectures, and Model Serving.
- Technical Leadership & Consultative Discovery: Lead architectural workshops, translate business requirements into technical roadmaps, and troubleshoot production bottlenecks alongside client teams.
- Cross-Functional Collaboration: Act as the primary technical bridge between Tiger Analytics delivery teams, client stakeholders, and Databricks field engineering teams to drive adoption and expand platform capabilities.
Requirements
- 5+ years of hands-on experience in Data Engineering, Applied AI/ML Engineering,
- Strong proficiency in Python and SQL, with deep hands-on mastery of Apache Spark (performance tuning, query optimization, cluster sizing, and memory management).
- Proven experience implementing Lakehouse architectures, including Delta Lake, Unity Catalog (governance, lineage, access control), and Databricks Workflows.
- Practical experience building and deploying AI/GenAI solutions on Databricks (e.g., Databricks AI/BI Genie, Mosaic AI, Vector Search, LLM integration, or MLflow).
- Experience migrating enterprise data workloads or modernizing legacy BI/data warehouses onto cloud platforms (AWS, Azure, or GCP).
- Exceptional communication and consultative skills, with a demonstrated track record of operating effectively in fast-paced, client-facing environments.
The FDE Mindset
- Strong customer-facing communication and sharp problem-solving instincts.
- Comfortable operating independently, navigating ambiguity, and moving fast from problem → solution → production.
- Equally at home with business and technical stakeholders.
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