Sign in to see your fit: the skills, experience and location this job asks for, checked against your profile.
Job description
Location: Bangalore, Karnataka, India Department: AI & Data Engineering
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
Most IT Jobs posts in Bangalore ask for 3–6 years of experience (41% of 3125 that say).
Hiring most for IT Jobs in Bangalore
Bosch Group156 open jobs
Trigent Software Private Limited151 open jobs
Brillio114 open jobs
Jobs asking for Apache Spark in Bangalore also ask for
Python76%
SQL60%
AWS56%
Databricks45%
Data Engineering45%
From live job posts on CoreHRx, updated 3 Nov 2026. Skill figures count posts that list their skills; salary figures only posts that show a salary.
Questions about this job
What is the salary for this Databricks Data Specialist job?
The post does not state the pay. As an estimate, comparable posts on CoreHRx advertise ₹14L – ₹25L a year (typical for IT Jobs in Bangalore, 286 posts).
Where is this job based?
In Bangalore (hybrid).
What kind of job is this?
It is a full time job.
How do I apply?
You apply on Brillio's website. CoreHRx takes you there after you sign in.
This guide is for Indian job seekers preparing for SQL rounds in data analyst, data engineer, backend developer and database roles, from freshers to experienced professionals. It has 60 questions in three levels: easy covers joins, grouping and NULLs; medium covers window functions, CTEs, indexes and isolation levels; hard covers locking, MVCC, query tuning and classic puzzles such as login streaks and medians. Answers use ANSI SQL, with MySQL 8 and PostgreSQL notes where the two differ. Write each query against your own sample tables first, then compare your result with the sample answer.
Questions you may be asked
What is the difference between WHERE and HAVING?
What is the difference between DELETE, TRUNCATE and DROP?
What is the difference between an INNER JOIN and a LEFT JOIN?
What is the difference between a primary key and a unique key?
