Databricks Developer / Data Architect
At a glance
Mid-level Data Engineering role at Fa Ewjt Saasfaprod1. Pune · full-time.
Pay not stated Data Engineering salary
dataroles summary, based on the employer's posting.
What you'll do
- Design Databricks Lakehouse architectures and data models for structured and semi-structured information
- Set up Databricks workspaces, clusters, access controls and federation connections across data sources
- Build batch and streaming ETL pipelines and reusable ingestion frameworks with PySpark, Spark SQL and Databricks workflows
- Tune Spark jobs and automate ETL deployment and monitoring to improve performance, cost and reliability
- Deploy data solutions on Azure and AWS, connect the named platforms, and support CI/CD and environment management
What you bring
- At least five years in data engineering or data architecture
- Hands-on Databricks experience, with strong Python, Spark, PySpark and SQL skills
- Knowledge of Lakehouse and Medallion approaches, data modeling and ETL/ELT design
- Experience with Azure and AWS, plus integrations such as PostgreSQL, DynamoDB or another listed platform
- Spark and Databricks tuning skills, along with version control and DevOps experience
Who this fits
This role suits a mid-level data engineer or architect who can build and optimize Databricks pipelines and platform designs. It is based in Pune with a hybrid arrangement.
From the employer
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
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
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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