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

• 6–12+ years as a Senior Data Engineer, 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.