Databricks Solution Architect – Pre‑Sales (Modern Data Platform / Lakehouse)

Company Description

Eservecloud Solutions Private Limited specializes in delivering cutting-edge technology solutions that empower organizations to optimize their operations and drive business growth. The company focuses on providing innovative tools and services in the cloud computing and data analytics domains. Eservecloud Solutions is committed to delivering robust and scalable solutions that address the dynamic needs of businesses. The company fosters a collaborative and inclusive work culture, where excellence and innovation thrive.

Role Description

We are seeking a highly accomplished Databricks Solution Architect (Pre‑Sales) to lead solutioning for enterprise data platform modernization initiatives and drive revenue through technical sales engagements. This role partners closely with Sales, Alliances, and Delivery teams to discover customer use cases, shape scalable solution architectures, lead demonstrations and proofs‑of‑concept (POCs), and produce high‑quality proposal artifacts that accelerate deal closure.The Solution Architect will act as a trusted advisor to clients across Databricks Lakehouse / Data Intelligence Platform, cloud‑native data platforms, governance, security, analytics, and AI‑ready architectures.

Work ExperienceOverall Experience

• 12–18 years of overall experience in data, analytics, big data, and cloud platforms
• 7–10+ years in solution architecture roles spanning data engineering, analytics, and platform modernization
• 5+ years in a customer‑facing pre‑sales / solutioning role, supporting RFPs, proposals, and POCs for large enterprise clients

Pre‑Sales & Solution Architecture Experience

• Proven experience leading pre‑sales technical engagements, including discovery workshops, requirement analysis, architecture definition, and executive‑level presentations
• End‑to‑end ownership of solution shaping for data platform programs, covering:
• Current‑state assessment and gap analysis
• Target architecture and migration roadmap
• Effort estimation, sizing models, assumptions, risks, and dependencies
• Hands‑on ownership of POCs / pilot engagements, including scope definition, success criteria, demo execution, and outcome articulation
• Strong experience supporting RFP/RFQ responses, creating architecture diagrams, solution narratives, delivery approaches, and commercial inputs in collaboration with sales and delivery teams

Databricks & Modern Data Platform Experience

• Hands‑on experience architecting solutions on Databricks Lakehouse / Data Intelligence Platform, including:
• Batch and streaming ingestion patterns
• Medallion (Bronze / Silver / Gold) architecture
• Delta Lake‑based storage and processing
• Unity Catalog‑driven governance and security
• Strong experience designing cloud‑native data platforms on Azure, AWS, or GCP, including storage, compute, networking, security, and cost considerations
• Experience integrating Databricks with the broader ecosystem such as BI tools, orchestration frameworks, CI/CD pipelines, and enterprise monitoring platforms

Leadership & Stakeholder Engagement

• Experience engaging with CxO, data leaders, and enterprise architects, translating business goals into scalable technical solutions
• Ability to articulate technology trade‑offs and architectural decisions to both technical and non‑technical stakeholders
• Experience mentoring junior architects/engineers and contributing reusable assets such as reference architectures, accelerators, and demo frameworks

Preferred / Domain Exposure

• Exposure to AI/ML and MLOps concepts and AI‑ready data platform architectures
• Experience driving large‑scale legacy modernization (EDW → Lakehouse, Hadoop/Spark → Databricks, BI modernization)
• Domain experience in BFSI, Insurance, Retail, Healthcare, or Telecom is a strong advantage
  

Key ResponsibilitiesPre‑Sales & Deal Shaping

• Lead customer discovery sessions to understand business objectives, current data landscapes, constraints, and success metrics
• Define target‑state architectures, solution options, and phased transformation roadmaps
• Drive technical evaluations and solution positioning in partnership with Account Executives
• Design and lead demonstrations, workshops, and POCs to validate architecture and value propositions
• Develop proposal‑quality deliverables including architecture diagrams, estimates, delivery approach, risks, assumptions, and dependencies
• Present solutions to senior technical and executive stakeholders with clear business value articulation

Databricks & Architecture Responsibilities

• Design end‑to‑end Lakehouse architectures for batch, near‑real‑time, and streaming workloads
• Define governance, security, and data access strategies using Unity Catalog
• Architect scalable ingestion, transformation, and orchestration patterns
• Recommend performance optimization and cost‑management strategies
• Define CI/CD, environment promotion, and automation patterns for data platforms
  

Key SkillsDatabricks & Lakehouse Platform

• Databricks Lakehouse / Data Intelligence Platform architecture
• Apache Spark (batch & streaming), Spark SQL, performance tuning
• Delta Lake (ACID transactions, schema evolution, time travel)
• Databricks Workflows / Jobs, cluster policies, workspace design
• Unity Catalog – data governance, access control, lineage, auditability

Modern Data Platform & Cloud

• Cloud‑native data architectures on Azure / AWS / GCP
• Data ingestion & integration patterns (batch, near‑real‑time, streaming)
• Data warehousing & analytics concepts (EDW modernization, ELT patterns)
• Integration with BI/Analytics tools (Power BI, Tableau, Looker)
• Data platform security, compliance, and non‑functional requirements

Pre‑Sales & Solutioning

• Technical discovery and use‑case framing
• Architecture definition, solution alternatives, and trade‑off analysis
• POC design and execution (scope, success metrics, demos)
• Proposal development – architecture diagrams, sizing, estimates, risks & assumptions
• Stakeholder communication from engineering teams to CxO audiences

Engineering & Enablement

• Programming/scripting: Python, SQL (working knowledge of Scala preferred)
• CI/CD concepts, repo‑based development, DevOps for data platforms
• Cost optimization, scalability, and reliability patterns
• Ability to create reusable reference architectures, accelerators, and demo assets
  

Certifications (Preferred / Good to Have)Databricks

• Databricks Certified Solutions Architect
• Databricks Certified Data Engineer (Associate / Professional)
• Databricks Certified Machine Learning Professional (nice to have)

Cloud Platforms

• Azure: Azure Solutions Architect Expert, Azure Data Engineer Associate
• AWS: AWS Solutions Architect (Associate / Professional), Data Analytics – Specialty
• GCP: Professional Data Engineer or Cloud Architect

Complementary (Optional)

• TOGAF or enterprise architecture frameworks
• FinOps / cloud cost management certifications
• Security & data governance certifications