Job Title: Databricks Solution Architect

Location: Remote

Employment Type: W2 Contract (No C2C)

About the Role

We are seeking an experienced Databricks Solution Architect with 10–12+ years of experience in data engineering, cloud data platforms, or solution architecture to lead the architecture, design, and delivery of enterprise-scale Databricks solutions.

This is a Databricks-focused role centered on Unity Catalog, Delta Lake, Lakeflow, Medallion Architecture, governance, performance optimization, and modern data engineering patterns.

You will work directly with client and delivery teams to translate complex business and technical requirements into scalable, secure, and production-ready solutions. The role combines hands-on architecture with technical leadership, including solution design, implementation guidance, architecture reviews, platform standards, and optimization across enterprise environments.

Key Responsibilities

- Architect and lead the implementation of end-to-end Databricks Lakehouse platforms using Delta Lake, Lakeflow Pipelines, MLflow, and modern Databricks platform capabilities.
- Design Medallion Architecture patterns across Bronze, Silver, and Gold layers for structured, semi-structured, batch, and streaming workloads.
- Establish enterprise governance patterns using Unity Catalog , including RBAC/ABAC, lineage, data classification, secure sharing, storage credentials, and external locations.
- Design scalable ETL/ELT and orchestration frameworks using Lakeflow Jobs, SQL Warehouses, Spark, PySpark, and reusable transformation patterns.
- Architect real-time and near-real-time data pipelines using Auto Loader, Structured Streaming, Kafka, Kinesis, Pub/Sub, and event-driven architectures.
- Design integration patterns between Databricks and cloud-native services across AWS, Azure, and GCP , as well as enterprise applications, APIs, and external data platforms.
- Define secure platform architecture across identity, networking, private connectivity, secrets, encryption, and access controls .
- Establish CI/CD and Infrastructure as Code patterns for Databricks using Terraform, Databricks CLI, Declarative Automation Bundles, GitHub Actions, Azure DevOps, or Jenkins .
- Optimize Spark workloads, Delta tables, SQL Warehouses, Photon-enabled workloads, and serverless compute for performance, reliability, and cost efficiency.
- Drive cloud and Databricks cost optimization across compute, storage, orchestration, and workload execution.
- Lead architecture reviews, technical design discussions,



and platform standards while mentoring engineering teams throughout implementation and delivery.
- Evaluate and introduce relevant Databricks capabilities such as Lakehouse Federation, serverless compute, AI/ML services, Vector Search, and model serving based on client and platform requirements.

Required Qualifications

- Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Engineering, Engineering, or a related technical field.
- 10+ years of experience across enterprise data engineering, cloud architecture, data platforms, or solution architecture.
- 5+ years of hands-on experience architecting, designing, and delivering solutions on the Databricks Lakehouse Platform .
- Strong hands-on experience with Databricks, Apache Spark, Delta Lake, Unity Catalog, and enterprise Lakehouse architecture .
- Proven experience designing and leading enterprise-scale Databricks implementations across one or more major cloud platforms.
- Experience implementing Unity Catalog for governance, lineage, RBAC/ABAC, data classification, secure access, and multi-team data management.
- Experience designing Medallion Architecture and scalable batch, streaming, and near-real-time data solutions.
- Hands-on cloud architecture experience with AWS, Azure, or GCP , including storage, compute, IAM, networking, and security services.
- Experience with Terraform, CI/CD, Infrastructure as Code, and Git-based deployment workflows for Databricks environments.
- Strong proficiency in PySpark and Spark SQL , including performance tuning, workload optimization, and large-scale pipeline design.
- Experience with Databricks Workflows, Lakeflow Pipelines, SQL Warehouses, Auto Loader, Structured Streaming, and serverless compute .
- Strong client-facing communication skills with experience leading architecture reviews, technical design discussions, and stakeholder decision-making.

Preferred Qualifications

- Experience with Scala-based Spark workloads in addition to PySpark and Spark SQL.
- Experience with orchestration and transformation frameworks such as Apache Airflow and dbt .
- Experience architecting streaming and event-driven solutions using Kafka,



Kinesis, Pub/Sub, Auto Loader, and Structured Streaming .
- Hands-on experience with Lakehouse Federation, Photon, serverless compute, and advanced Databricks performance optimization .
- Experience with MLflow, model serving, Vector Search, or other Databricks AI/ML capabilities .
- Experience designing and managing multi-workspace, multi-setting, or multi-domain Databricks architectures .
- Familiarity with advanced Databricks governance patterns including ABAC, governed tags, secure data sharing, storage credentials, and external locations .
- Experience integrating Databricks with enterprise applications, APIs, relational databases, SaaS platforms, and cloud-native services.
- Experience with containerization and supporting data services using Docker, FastAPI, or Flask .
- Databricks certifications are preferred; cloud architecture certifications across AWS, Azure, or GCP and TOGAF are a plus.

Interview Process Initial Screening

1. A brief conversation with the CloudTech Innovations team to review your background, Databricks experience, work authorization, compensation alignment, and overall fit.

Client Introduction / Phone Screening

1. A short introductory call with the client or delivery team to discuss your background, communication style, high-level technical experience, and alignment with the engagement.

Technical Deep-Dive

1. A focused technical interview with the implementation partner covering Databricks architecture, Unity Catalog, Spark, governance, Terraform/CI/CD, streaming, performance optimization, and enterprise solution design.

Final Client Interview

1. A final discussion with the end client to confirm technical alignment and project fit. Additional rounds may be required depending on the specific engagement.

After the Interview Process Candidates who successfully complete the interview process will be aligned to the client engagement that best matches their technical background and experience. Final selection is subject to client approval, project availability, and completion of any required onboarding or background verification.

Once selected, the CloudTech Innovations team will coordinate offer details, onboarding, project start expectations, and client-specific requirements.

Project Flexibility: If a candidate is a strong technical fit but not selected for the initial engagement, they may be considered for other active client opportunities that align with their Databricks and cloud architecture experience.

📌 Databricks Solution Architect (Bengaluru)
🏢 Cloudtech-innovations
📍 Bengaluru