Forward Deployment Engineer

Job description

**Job Description: Key Responsibilities** **1. Deployment & Infrastructure Engineering** - Deploy EXLdata.ai in client-owned AWS/Azure/GCP environments. - Configure networking, security, CI/CD, Kubernetes, API gateways, and identity integration. - Troubleshoot environment, infra, IAM, and pipeline-related issues. - Lead cloud-level optimizations (scaling, cost, performance tuning). **2. Data Engineering & Pipeline Enablement** - Build, customize, and optimize data pipelines using **PySpark, SQL, Databricks, Snowflake** , or native hyperscaler data services. - Integrate platform agents into client workflows (Data Migration, DQ, DataOps, Annotation). - Assist client SMEs in onboarding data sources, targets, and transformations. **3. Value Realization & Client Enablement** - Serve as the **technical anchor** for first-of-kind deployments at each client. - Ensure clients see measurable value from agent-driven automation (SLA reduction, pipeline acceleration, DQ uplift, migration speed). - Provide hands-on support across discovery, configuration, runbooks, and UAT. **4. GenAI Agent Integration** - Work with product engineering on integrating new GenAI agents into client pipelines. - Tailor agent behaviors, triggers, and workflows for domain-specific use cases. - Share field insights that shape our agent roadmap. **5. Product Innovation & Feedback Loop** - Act as the “voice of the customer” for the EXLdata.ai product team. - Identify enhancements, feature gaps, and new accelerator ideas. - Participate in internal sprints, tooling improvements, and platform hardening. **6. Managed Service / White-Glove Model** - Support deployments in EXL-hosted private cloud environments. - Serve as the first line of operational excellence for premium clients. Lead operational reliability, monitoring, and support SLAs. **Required Skills & Experience** **Technical Expertise** - 12+ years as a **Senior Data Engineer / Architect** , Forward Deployment Engineer, or Platform Engineer. - Strong hands-on experience with **at least one hyperscaler** (AWS or Azure or GCP). - Deep expertise in: - **PySpark** , SQL, Python - **Databricks / Snowflake** (one mandatory, both preferred) - Cloud data services (Kinesis, Glue, Redshift, Synapse, BigQuery, DataProc, etc.) - Kubernetes, Docker, CI/CD - IAM, VPC, private networking, secrets, API management **Delivery & Client Facing Skills** - Demonstrated ability to **work directly with client engineering teams** . - Comfortable running design discussions, debugging sessions, and deployment workshops. - Strong communication skills; able to simplify technical topics for business audiences. - Ability to operate independently with a **consulting mindset and ownership mentality** . **GenAI & Multi-Agent Curiosity** - Exposure to LLMs, agent tooling (LangChain, LangGraph, CrewAI, etc.), or willingness to learn fast. - Strong interest in how AI can automate data engineering and governance. **Mindset & Attributes** - “Can-do” attitude; thrives in ambiguity. - Fast learner; bias for action. - Team player who collaborates across product, engineering, and client teams. - Customer-first orientation and passion for delivering measurable outcomes. **Responsibilities: Key Responsibilities** **1. Deployment & Infrastructure Engineering** - Deploy EXLdata.ai in client-owned AWS/Azure/GCP environments. - Configure networking, security, CI/CD, Kubernetes, API gateways, and identity integration. - Troubleshoot environment, infra, IAM, and pipeline-related issues. - Lead cloud-level optimizations (scaling, cost, performance tuning). **2. Data Engineering & Pipeline Enablement** - Build, customize, and optimize data pipelines using **PySpark, SQL, Databricks, Snowflake** , or native hyperscaler data services. - Integrate platform agents into client workflows (Data Migration, DQ, DataOps, Annotation). - Assist client SMEs in onboarding data sources, targets, and transformations. **3. Value Realization & Client Enablement** - Serve as the **technical anchor** for first-of-kind deployments at each client. - Ensure clients see measurable value from agent-driven automation (SLA reduction, pipeline acceleration, DQ uplift, migration speed). - Provide hands-on support across discovery, configuration, runbooks, and UAT. **4. GenAI Agent Integration** - Work with product engineering on integrating new GenAI agents into client pipelines. - Tailor agent behaviors, triggers, and workflows for domain-specific use cases. - Share field insights that shape our agent roadmap. **5. Product Innovation & Feedback Loop** - Act as the “voice of the customer” for the EXLdata.ai product team. - Identify enhancements, feature gaps, and new accelerator ideas. - Participate in internal sprints, tooling improvements, and platform hardening. **6. Managed Service / White-Glove Model** - Support deployments in EXL-hosted private cloud environments. - Serve as the first line of operational excellence for premium clients. - Lead operational reliability, monitoring, and support SLAs. **Qualifications: Technical Expertise** - 12+ years as a **Senior Data Engineer / Architect** , Forward Deployment Engineer, or Platform Engineer. - Strong hands-on experience with **at least one hyperscaler** (AWS or Azure or GCP). - Deep expertise in: - **PySpark** , SQL, Python - **Databricks / Snowflake** (one mandatory, both preferred) - Cloud data services (Kinesis, Glue, Redshift, Synapse, BigQuery, DataProc, etc.) - Kubernetes, Docker, CI/CD - IAM, VPC, private networking, secrets, API management **Delivery & Client Facing Skills** - Demonstrated ability to **work directly with client engineering teams** . - Comfortable running design discussions, debugging sessions, and deployment workshops. - Strong communication skills; able to simplify technical topics for business audiences. - Ability to operate independently with a **consulting mindset and ownership mentality** . **GenAI &