Role Overview : The Data Architect - Databricks is responsible for designing and implementing scalable, secure, and high-performance data architectures leveraging the Databricks Lakehouse platform. The role requires deep expertise in data engineering, cloud integration, data modeling, and distributed data processing frameworks such as Apache Spark.

Key Responsibilities : Architecture & Engineering : Design and implement scalable data architectures using Databricks Lakehouse principles.

Develop and optimize ETL/ELT pipelines for batch and streaming data ingestion.

Build and manage data solutions using Delta Lake and medallion architecture.

Develop data pipelines using PySpark, Scala, and SQL.

Implement structured streaming solutions using Azure Databricks.

Build and query Delta Lake storage solutions.

Flatten nested structures and process complex datasets using Spark transformations.

Optimize Spark jobs for performance and cost efficiency.

Establish reusable frameworks, templates, and best practices for enterprise data engineering.

Cloud & Integration : Integrate Databricks with cloud platforms such as:

- Microsoft Azure
- Amazon Web Services
- Google Cloud Platform

Work with Azure services including:

- Azure Data Factory
- Azure Data Lake Storage
- Azure Synapse Analytics
- Azure SQL Data Warehouse

Implement orchestration using Azure Data Factory and Databricks Jobs.

Transfer and process data using Spark pools and PySpark connectors.

Security, Governance & Operations : Design secure Databricks environments including RBAC and Unity Catalog.

Ensure data security, compliance, and enterprise governance standards.

Implement CI/CD pipelines and Git-based version control for Databricks workflows.

Perform cluster sizing, cost optimization, and performance tuning.

Monitor and troubleshoot Databricks environments for reliability.

Strategy, Leadership & Presales :



Define enterprise data strategies and roadmaps.

Evaluate emerging tools and technologies for adoption.

Architect solution frameworks aligned with business goals.

Collaborate with sales teams on presales activities including solution design and customer discussions.

Engage stakeholders and provide technical leadership across cross-functional teams.

Required Technical Skills : 12+ years of experience in data engineering, including hands-on experience with Databricks or Apache Spark-based platforms.

Strong expertise in PySpark, Spark SQL, and Scala.

Experience processing streaming data and working with Apache Kafka (preferred).

Proven experience building batch and real-time ingestion frameworks.

Deep knowledge of Delta Lake, Delta Live Tables, and medallion architecture.

Familiarity with enterprise Databricks setups including Unity Catalog and governance models.

Experience with CI/CD, DevOps, DataOps, and AIOps frameworks.

Knowledge of MLOps, data observability, and BI tools.

Experience with Snowflake and/or Microsoft Fabric (added advantage).

Strong understanding of data modeling, distributed systems, and cloud integration.

Eligibility Criteria : Bachelor’s degree in Computer Science, Information Technology, or related field.

Proven experience as a Databricks Architect or similar senior data engineering role.

Complete understanding of Databricks platform architecture.

Databricks certification (e.g., Certified Data Engineer).

Expertise in Python, Scala, SQL, and/or R.

Experience with AWS, Azure, GCP, or Alibaba Cloud.

Experience in security implementation and cluster configuration.

Strong analytical, problem-solving, and communication skills.

Preferred Competencies : Experience working in large-scale enterprise environments.

Robust stakeholder management and consulting skills.

Ability to drive innovation and organizational data transformation initiatives.

**What next?

📌 Data Architect (Bengaluru)
🏢 Axiom Technologies
📍 Bengaluru