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Databricks Developer / Data Architect
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Job Info
- Job Identification15671
- Posting Date06/22/2026, 07:46 AM
- Job RoleData architect-Architect
- Experience (In Years)6-9
- Job LocationIndia
Job Description
Job Description: Databricks Developer / Data Architect
Position Title
Databricks Developer / Data Architect
Location
Hybrid/ Remote
Employment Type
Full-Time / Contract
Job Summary
We are seeking an experienced Databricks Developer / Data Architect to design, implement, and optimize modern data platforms and ETL pipelines using Databricks and cloud-native technologies. The ideal candidate will have strong expertise in data architecture, Lakehouse implementation, Medallion Architecture, and scalable ETL development across Azure and AWS environments.
The role involves setting up Databricks workspaces, configuring data integrations and Lakehouse Federation, building enterprise-grade ETL workflows, and enabling high-performance analytics solutions.
Key Responsibilities
Data Architecture & Modeling
- Design and implement scalable enterprise data architectures using Databricks Lakehouse platform
- Configure and manage Databricks workspaces, clusters, access controls, and governance
- Implement Medallion Architecture (Bronze, Silver, Gold layers) for data processing and analytics
- Set up and manage Lakehouse Federation and data connectors for multi-source integration
- Develop logical and physical data models for structured and semi-structured datasets
- Ensure data quality, security, scalability, and performance optimization
ETL Development
- Develop and maintain scalable ETL/ELT pipelines using PySpark, Spark SQL, and Databricks workflows
- Build reusable data ingestion frameworks for batch and streaming workloads
- Optimize Spark jobs for performance, cost efficiency, and reliability
- Integrate data from relational and NoSQL databases, cloud platforms, and external systems
- Automate deployment and monitoring of ETL workflows
Cloud & Platform Engineering
- Work with Azure and AWS cloud services to deploy and manage data solutions
- Configure integrations with Snowflake, Postgres, MongoDB, DynamoDB, Cloudera, and Domino Server
- Support CI/CD, infrastructure automation, and environment management
- Collaborate with cross-functional teams including Data Scientists, Analysts, and Business stakeholders
Required Skills & Qualifications
- 5+ years of experience in Data Engineering / Data Architecture
- Strong hands-on experience with Databricks platform
- Expertise in:
- Python
- Apache Spark
- PySpark
- SQL
- Strong understanding of:
- Lakehouse Architecture
- Medallion Architecture
- Data Modeling
- ETL/ELT Design Patterns
- Experience with cloud platforms:
- Microsoft Azure
- AWS
- Experience integrating with:
- PostgreSQL
- DynamoDB
- MongoDB
- Snowflake
- Cloudera
- Domino Server
- Knowledge of performance tuning and optimization in Spark/Databricks
- Experience with version control and DevOps practices
Preferred Qualifications
- Databricks Certification(s)
- Experience with Delta Lake and Unity Catalog
- Familiarity with streaming frameworks and real-time data processing
- Knowledge of data governance and security best practices
- Experience in Agile/Scrum delivery models
Technical Stack
- Databricks
- Python
- Spark / PySpark
- SQL
- Azure
- AWS
- Snowflake
- PostgreSQL
- MongoDB
- DynamoDB
- Cloudera
- Domino Server
Soft Skills
- Strong analytical and problem-solving abilities
- Excellent communication and stakeholder management skills
- Ability to work independently and collaboratively in fast-paced environments
- Strong documentation and solution design capabilities
Nice-to-Have
- Experience with Terraform or Infrastructure as Code
- Exposure to ML/data science platforms
- Experience with orchestration tools such as Airflow or Azure Data Factory
Responsibilities
Job Description: Databricks Developer / Data Architect
Position Title
Databricks Developer / Data Architect
Location
Hybrid/ Remote
Employment Type
Full-Time / Contract
Job Summary
We are seeking an experienced Databricks Developer / Data Architect to design, implement, and optimize modern data platforms and ETL pipelines using Databricks and cloud-native technologies. The ideal candidate will have strong expertise in data architecture, Lakehouse implementation, Medallion Architecture, and scalable ETL development across Azure and AWS environments.
The role involves setting up Databricks workspaces, configuring data integrations and Lakehouse Federation, building enterprise-grade ETL workflows, and enabling high-performance analytics solutions.
Key Responsibilities
Data Architecture & Modeling
- Design and implement scalable enterprise data architectures using Databricks Lakehouse platform
- Configure and manage Databricks workspaces, clusters, access controls, and governance
- Implement Medallion Architecture (Bronze, Silver, Gold layers) for data processing and analytics
- Set up and manage Lakehouse Federation and data connectors for multi-source integration
- Develop logical and physical data models for structured and semi-structured datasets
- Ensure data quality, security, scalability, and performance optimization
ETL Development
- Develop and maintain scalable ETL/ELT pipelines using PySpark, Spark SQL, and Databricks workflows
- Build reusable data ingestion frameworks for batch and streaming workloads
- Optimize Spark jobs for performance, cost efficiency, and reliability
- Integrate data from relational and NoSQL databases, cloud platforms, and external systems
- Automate deployment and monitoring of ETL workflows
Cloud & Platform Engineering
- Work with Azure and AWS cloud services to deploy and manage data solutions
- Configure integrations with Snowflake, Postgres, MongoDB, DynamoDB, Cloudera, and Domino Server
- Support CI/CD, infrastructure automation, and environment management
- Collaborate with cross-functional teams including Data Scientists, Analysts, and Business stakeholders
Required Skills & Qualifications
- 5+ years of experience in Data Engineering / Data Architecture
- Strong hands-on experience with Databricks platform
- Expertise in:
- Python
- Apache Spark
- PySpark
- SQL
- Strong understanding of:
- Lakehouse Architecture
- Medallion Architecture
- Data Modeling
- ETL/ELT Design Patterns
- Experience with cloud platforms:
- Microsoft Azure
- AWS
- Experience integrating with:
- PostgreSQL
- DynamoDB
- MongoDB
- Snowflake
- Cloudera
- Domino Server
- Knowledge of performance tuning and optimization in Spark/Databricks
- Experience with version control and DevOps practices
Preferred Qualifications
- Databricks Certification(s)
- Experience with Delta Lake and Unity Catalog
- Familiarity with streaming frameworks and real-time data processing
- Knowledge of data governance and security best practices
- Experience in Agile/Scrum delivery models
Technical Stack
- Databricks
- Python
- Spark / PySpark
- SQL
- Azure
- AWS
- Snowflake
- PostgreSQL
- MongoDB
- DynamoDB
- Cloudera
- Domino Server
Soft Skills
- Strong analytical and problem-solving abilities
- Excellent communication and stakeholder management skills
- Ability to work independently and collaboratively in fast-paced environments
- Strong documentation and solution design capabilities
Nice-to-Have
- Experience with Terraform or Infrastructure as Code
- Exposure to ML/data science platforms
- Experience with orchestration tools such as Airflow or Azure Data Factory
Qualifications
Preferred Qualifications
- Databricks Certification(s)
- Experience with Delta Lake and Unity Catalog
- Familiarity with streaming frameworks and real-time data processing
- Knowledge of data governance and security best practices
- Experience in Agile/Scrum delivery models
Technical Stack
- Databricks
- Python
- Spark / PySpark
- SQL
- Azure
- AWS
- Snowflake
- PostgreSQL
- MongoDB
- DynamoDB
- Cloudera
- Domino Server
Soft Skills
- Strong analytical and problem-solving abilities
- Excellent communication and stakeholder management skills
- Ability to work independently and collaboratively in fast-paced environments
- Strong documentation and solution design capabilities
Nice-to-Have
- Experience with Terraform or Infrastructure as Code
- Exposure to ML/data science platforms
- Experience with orchestration tools such as Airflow or Azure Data Factory
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