Key Responsibilities
- 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).
- 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.
- 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.
- 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.
- 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.
- 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
- 612+ 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.
