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Brillio-2

Databricks Data Specialist

Bangalore, Karnataka, India

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What you’ll work on

Full posting

We are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform.

We are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform.

  • Design, develop, and maintain end-to-end data pipelines using Databricks and PySpark.

  • Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.

  • Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features.

From the employer’s posting
We are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform. The ideal candidate will possess strong expertise in Databricks, PySpark, and SQL, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.
Key Responsibilities Design, develop, and maintain end-to-end data pipelines using Databricks and PySpark. Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.
Design, develop, and maintain end-to-end data pipelines using Databricks and PySpark. Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers. Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features.
Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers. Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features. Create, monitor, and optimize Delta Live Tables (DLT) pipelines.

What you’ll bring

All qualifications

Core experience

  • Strong expertise in Databricks, PySpark, and SQL
  • Hands-on Delta Lake and Delta Live Tables experience
  • Experience with Structured Streaming and Auto Loader
  • Lakehouse data modeling skills
  • Bachelor's or Master's degree in Computer Science, Data Engineering, IT, or related field
Qualification wording
strong expertise in Databricks, PySpark, and SQL
Delta Lake Delta Live Tables (DLT)
Structured Streaming Auto Loader
Lakehouse Data Modeling
Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.
Education & alternatives
Qualifications - Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline. - Proven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.

Tools in this posting

  • SQL
  • AWS
  • Azure
  • BigQuery
  • Databricks
  • dbt
  • Delta
  • Fivetran
  • Google Cloud (GCP)
  • Kafka
  • Power BI
  • Spark
  • Airflow
  • PySpark
Source — Tool mentions in context
Role Overview We are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform. The ideal candidate will possess strong expertise in Databricks, PySpark, and SQL, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization. Key Responsibilities
- Establish and enforce data governance, security, and access controls using Unity Catalog. - Optimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability. - Collaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.
- Auto Loader - SQL - Lakehouse Data Modeling
Key Technologies Databricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI Specialization
Key Technologies Databricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI Employment type
- Microsoft Fabric AWS Ecosystem - AWS Glue
AWS Ecosystem - AWS Glue - AWS Lambda
- AWS Glue - AWS Lambda - AWS Step Functions
- AWS Lambda - AWS Step Functions Data Engineering & Integration
Preferred Skills (Good to Have) Azure Ecosystem - Azure Data Factory (ADF)
Azure Ecosystem - Azure Data Factory (ADF) - Azure Synapse Analytics
- Azure Data Factory (ADF) - Azure Synapse Analytics - Microsoft Purview
GCP - BigQuery Qualifications
Primary Skills Databricks Engineer Role Overview
Key Responsibilities - Design, develop, and maintain end-to-end data pipelines using Databricks and PySpark. - Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.
- Develop and manage real-time data processing solutions using Structured Streaming. - Orchestrate, schedule, and monitor data workflows using Databricks Workflows. - Design and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.
Required Skills (Must Have) - Databricks Platform - Delta Lake
- Unity Catalog - Databricks Workflows - PySpark and Apache Spark
Specialization - Databricks Engineering: Lead Data Engineer Job requirements
Job requirements Databricks Engineer Role Overview
- Apache Airflow - DBT - Fivetran
- Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers. - Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features. - Create, monitor, and optimize Delta Live Tables (DLT) pipelines.
- Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features. - Create, monitor, and optimize Delta Live Tables (DLT) pipelines. - Implement scalable and efficient data ingestion processes using Auto Loader.
- Databricks Platform - Delta Lake - Delta Live Tables (DLT)
- Delta Lake - Delta Live Tables (DLT) - Unity Catalog
- DBT - Fivetran - Informatica
- Alation GCP - BigQuery
Streaming & Analytics - Apache Kafka - Power BI
- Apache Kafka - Power BI Data Governance
- Databricks Workflows - PySpark and Apache Spark - Structured Streaming
Data Engineering & Integration - Apache Airflow - DBT

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Status in our records
Live
First seen by us
Aug 14, 2026 · 65d ago
Recorded sightings
138
Last seen by us
Oct 17, 2026
Employer says posted
Aug 10, 2026

These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.

Job description

View original posting ↗

Data Specialist

Primary Skills

    Databricks Engineer

    Role Overview

    We are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform. The ideal candidate will possess strong expertise in Databricks, PySpark, and SQL, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.

    Key Responsibilities

  • Design, develop, and maintain end-to-end data pipelines using Databricks and PySpark.
  • Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.
  • Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features.
  • Create, monitor, and optimize Delta Live Tables (DLT) pipelines.
  • Implement scalable and efficient data ingestion processes using Auto Loader.
  • Develop and manage real-time data processing solutions using Structured Streaming.
  • Orchestrate, schedule, and monitor data workflows using Databricks Workflows.
  • Design and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.
  • Establish and enforce data governance, security, and access controls using Unity Catalog.
  • Optimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability.
  • Collaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.

    • Required Skills (Must Have)

  • Databricks Platform
  • Delta Lake
  • Delta Live Tables (DLT)
  • Unity Catalog
  • Databricks Workflows
  • PySpark and Apache Spark
  • Structured Streaming
  • Auto Loader
  • SQL
  • Lakehouse Data Modeling
  • Strong understanding of data engineering best practices and scalable data architectures

    • Preferred Skills (Good to Have)

    Azure Ecosystem

  • Azure Data Factory (ADF)
  • Azure Synapse Analytics
  • Microsoft Purview
  • Microsoft Fabric

    • AWS Ecosystem

  • AWS Glue
  • AWS Lambda
  • AWS Step Functions

    • Data Engineering & Integration

  • Apache Airflow
  • DBT
  • Fivetran
  • Informatica

    • Streaming & Analytics

  • Apache Kafka
  • Power BI

    • Data Governance

  • Collibra
  • Alation

    • GCP

  • BigQuery

    • Qualifications

  • Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.
  • Proven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.
  • Strong analytical, troubleshooting, and problem-solving capabilities.
  • Experience working in agile and collaborative environments.
  • Excellent communication and stakeholder management skills.

    • Preferred Candidate Profile

  • Hands-on experience with modern Lakehouse architectures and enterprise-scale data platforms.
  • Strong understanding of data governance, security, and compliance frameworks.
  • Experience delivering both batch and real-time data processing solutions.
  • Ability to work independently while collaborating effectively across global teams.

    • Key Technologies

    Databricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI

Specialization

  • Databricks Engineering: Lead Data Engineer

Job requirements

    Databricks Engineer

    Role Overview

    We are seeking a highly skilled Databricks Engineer to design, develop, and optimize scalable data engineering and analytics solutions on the Databricks Lakehouse Platform. The ideal candidate will possess strong expertise in Databricks, PySpark, and SQL, with hands-on experience building batch and real-time data pipelines, implementing Lakehouse architectures, and ensuring data governance and performance optimization.

    Key Responsibilities

  • Design, develop, and maintain end-to-end data pipelines using Databricks and PySpark.
  • Build and implement Lakehouse architectures utilizing Bronze, Silver, and Gold data layers.
  • Develop and manage Delta Lake solutions with ACID transactions, schema enforcement, and data reliability features.
  • Create, monitor, and optimize Delta Live Tables (DLT) pipelines.
  • Implement scalable and efficient data ingestion processes using Auto Loader.
  • Develop and manage real-time data processing solutions using Structured Streaming.
  • Orchestrate, schedule, and monitor data workflows using Databricks Workflows.
  • Design and implement Lakehouse data models to support reporting, analytics, and business intelligence requirements.
  • Establish and enforce data governance, security, and access controls using Unity Catalog.
  • Optimize Spark jobs, SQL queries, and overall platform performance to ensure efficiency and scalability.
  • Collaborate with cross-functional teams, including data analysts, architects, and business stakeholders, to deliver high-quality data solutions.

    • Required Skills (Must Have)

  • Databricks Platform
  • Delta Lake
  • Delta Live Tables (DLT)
  • Unity Catalog
  • Databricks Workflows
  • PySpark and Apache Spark
  • Structured Streaming
  • Auto Loader
  • SQL
  • Lakehouse Data Modeling
  • Strong understanding of data engineering best practices and scalable data architectures

    • Preferred Skills (Good to Have)

    Azure Ecosystem

  • Azure Data Factory (ADF)
  • Azure Synapse Analytics
  • Microsoft Purview
  • Microsoft Fabric

    • AWS Ecosystem

  • AWS Glue
  • AWS Lambda
  • AWS Step Functions

    • Data Engineering & Integration

  • Apache Airflow
  • DBT
  • Fivetran
  • Informatica

    • Streaming & Analytics

  • Apache Kafka
  • Power BI

    • Data Governance

  • Collibra
  • Alation

    • GCP

  • BigQuery

    • Qualifications

  • Bachelor's or Master's degree in Computer Science, Data Engineering, Information Technology, or a related discipline.
  • Proven experience in designing and implementing cloud-based data engineering solutions and scalable data pipelines.
  • Strong analytical, troubleshooting, and problem-solving capabilities.
  • Experience working in agile and collaborative environments.
  • Excellent communication and stakeholder management skills.

    • Preferred Candidate Profile

  • Hands-on experience with modern Lakehouse architectures and enterprise-scale data platforms.
  • Strong understanding of data governance, security, and compliance frameworks.
  • Experience delivering both batch and real-time data processing solutions.
  • Ability to work independently while collaborating effectively across global teams.

    • Key Technologies

    Databricks | PySpark | Apache Spark | Delta Lake | Delta Live Tables (DLT) | Unity Catalog | Structured Streaming | Auto Loader | SQL | Lakehouse Architecture | Azure | AWS | Airflow | Kafka | Power BI

Employment type

Employee

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Source details

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Pay

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Location & working pattern

Bangalore, Karnataka, India

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