We are looking for an experienced Data Solution Architect to design, architect, and implement large-scale data platforms and analytics solutions. The ideal candidate will have strong hands-on experience in Databricks, Azure, and Google Cloud Platform (GCP), along with the ability to create animated/visual data stories for technical and business audiences.

Candidate will play a key role in defining data strategy, optimizing data pipelines, ensuring platform scalability, and developing reusable frameworks. ⸻

Required Skills & Experience

• 10–12+ years overall in data engineering, analytics, or architecture roles.

• 2–4+ years strong hands-on experience in Databricks (mandatory).

• Deep experience with Azure and GCP cloud ecosystems.

• Strong expertise in Spark, PySpark, SQL, Python.

• Proficiency in data modeling, ETL frameworks, and distributed data systems.

• Experience with streaming technologies (Kafka, Event Hub, Pub/Sub).

• Ability to design and present animated architecture and data flows.

• Strong communication and stakeholder management skills.

Key Responsibilities

1. Architecture & Solution Design

• Design end-to-end data lakehouse, data warehouse, and analytics architectures using Databricks, Azure, and GCP.

• Build scalable and cost-optimized architectures for ingestion, transformation, streaming, and ML workloads.

• Define and enforce architecture standards, patterns, and governance models.

• Translate business requirements into logical and physical data models.

2. Databricks Expertise

• Architect and optimize Databricks Lakehouse solutions using Delta Lake, Unity Catalog, Databricks SQL, MLflow, Auto Loader, and DLT.

• Implement advanced transformations using PySpark, Spark SQL, and notebooks.

• Design job orchestration using Databricks Workflows or other orchestration tools.

3. Cloud Platform Ownership

Azure

• Azure Data Factory, Azure Data Lake Storage (ADLS), Azure Synapse, Azure Functions, Event Hub, Azure DevOps, Azure Kubernetes Service (AKS).

• Architecture of secure and scalable data systems on Azure.

GCP

• BigQuery, Cloud Storage, Dataflow, Pub/Sub, Dataproc, Cloud Composer.

• Hands-on experience designing lakehouse/analytics solutions on GCP.

4. Data Engineering & Integration

• Lead development of batch and real-time data pipelines using Spark, ADF, Dataflow, or Databricks Workflows.

• Implement ETL/ELT frameworks, CI/CD, and reusable components.

6. Security & Governance

• Implement data governance frameworks including Unity Catalog, encryption, and compliance.

• Ensure best practices for cost optimization, monitoring, and performance tuning.