Databricks Platform & Data Engineer (CoE)

Key Responsibilities

Platform Engineering & Architecture

Design and deploy enterprise-scale Databricks Lakehouse platforms across AWS/Azure

Establish secure, governed environments using Unity Catalog, role-based access controls, and data lineage

Define platform standards, reusable patterns, and guardrails for scalable adoption

Optimize platform performance, cost, and reliability for production workloads

Implement CI/CD, environment promotion, and DevOps automation for Databricks

Databricks Feature Enablement

Lead Adoption Of Modern Databricks Capabilities Including

Unity Catalog: Centralized governance, access control, lineage

Dataflow / declarative pipelines: build scalable ingestion and transformation frameworks

Genie / AI-assisted development: accelerate developer productivity and data accessibility

Enable AI/BI dashboards, model serving, and advanced analytics use cases

Data Engineering & Use Case Delivery

Build and optimize batch and streaming data pipelines using Spark and Delta Lake

Develop data products and domain-oriented pipelines aligned to enterprise data strategies

Lead end-to-end use case delivery, from requirements to production deployment

Drive data quality, observability, and pipeline reliability

Client Engagement & Advisory

Act as a trusted advisor to client stakeholders (architecture, data, risk, and business teams)

Translate business requirements into technical architecture and delivery roadmaps

Lead workshops, solution design sessions, and platform adoption strategies

Support proposals, solutioning, and client innovations within regulated industries

CoE Contribution

Build and contribute to NTT DATA accelerators, frameworks, and reusable assets

Define best practices, reference architectures, and playbooks for enterprise Databricks adoption

Mentor junior engineers and support capability building across the CoE

Required Qualifications

12+ years of experience in data engineering, platform engineering, or data architecture

5+ years hands-on experience with Databricks in large enterprise environments

Deep expertise in: -

• Apache Spark (Scala/Python)
• Delta Lake and Lakehouse architectures
• Databricks workspace setup, cluster policies, and job orchestration
• Strong experience with cloud platforms (AWS, Azure, or GCP)
• Experience implementing data governance, security, and compliance controls
• Proven ability to design and deliver scalable, production-grade data platforms
• Strong client-facing and communication skills

Experience in financial services or other regulated industries

Familiarity with data governance frameworks, regulatory reporting, and risk data environments

Databricks Certifications (e.g., Data Engineer, Solutions Architect) Experience With

Real-time streaming (Kafka, Structured Streaming)

MLOps / Model Serving

Data marketplace / data product architectures

Exposure to AI-assisted development workflows and agent-based tooling