Senior Data Engineer

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.
  
  

• 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
  
  

• 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.
  
  

• 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.
  
  

• “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.