Platform Solution Architect

Platform Solution Architect – Azure & Databricks

Bangalore - Hybrid

Fulltime role

Enterprise Lakehouse Platform | Architecture, Frameworks, Governance & Observability

1. Role Summary

12+ years of experience Technical Solution Architect, the role owns design and Implement platform Observability, reusable frameworks, data quality and observability design, governance patterns and engineering standards

You will define how data is ingested and monitored, modelled, monitored and operated on a scale, and set the technical foundations that multiple engineering teams build on.

1. Platform level Observability Design and framework
2. Data Quality Framework
3. Platform FinOps and Monitoring design

2. Key Responsibilities

Platform Architecture & Solution Design

• Define Lakehouse and Medallion standards — Delta design, partitioning, file layout, schema evolution and curated/semantic modelling.
• Build observability into the architecture — control planes, audit and lineage capture designed in, not retro-fitted.
• Produce reference architecture, HLD/LLD and design decision records; lead solution and design reviews.

Reusable Framework Engineering

• Architect metadata-driven Framework— config-driven source onboarding with control tables and no bespoke code per feed.
• Design reusable ELT/transformation frameworks with standard error handling, restartability, idempotency and audit logging.
• Embed framework-level observability hooks — run telemetry, row-level audit counts, watermark and SLA tracking emitted by default.

Data Quality & Observability

• Design the platform data quality framework — rule catalogue, validation layers, quarantine, reconciliation and quality scoring across Medallion layers.
• Architect end-to-end data observability — freshness, completeness, volume anomaly, schema drift, lineage and job health.
• Define observability instrumentation standards using Databricks System Tables, job/cluster telemetry, Delta history and log analytics.
• Own platform health dashboards covering pipeline reliability, data quality trend and operational KPIs.

Engineering Standards

• Define CI/CD and environment promotion for Databricks assets using Azure DevOps, Git and automated testing.
• Instrument deployment observability — release traceability, environment drift detection and post-deployment validation.

3. Required Skills

Databricks & Lakehouse (Core)

• Enterprise-scale Azure Databricks architecture — workspace design, compute strategy, job orchestration, workload isolation.
• Unity Catalog design and rollout — governance model, fine-grained access control, lineage.

Frameworks, Data Quality & Observability

• Metadata-driven ingestion and reusable ELT framework design adopted across multiple teams.
• Data quality framework design — rules, validation, quarantine, reconciliation, quality metrics.
• Data and pipeline observability design — freshness, completeness, anomaly and schema-drift detection.
• Operational observability using Databricks System Tables, job telemetry, Delta history and Azure Monitor / Log Analytics.

Architecture

• HLD/LLD authorship, architecture diagramming and design authority across delivery teams.
• Translating ambiguous business requirements into scalable, governed technical solutions.
• Technical leadership and mentoring across Data Engineering, Security, DevOps and Cloud teams.

4. Experience & Qualifications

• 12+ years overall in data engineering and data platform roles, including 5+ years in a platform or solution architecture capacity.
• Ownership of at least one enterprise-scale Azure Databricks platform from architecture through production operation.
• Proven design of reusable frameworks, data quality and observability capabilities adopted by multiple teams.
• Experience operating across Data Engineering, Security, DevOps and Cloud functions in a matrixed enterprise.