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Databricks Data Specialist - R01569707

Brillio • Blockchain & Crypto • AI Consulting & Integration • Bachelor's Degree • Python • SQL • Power BI • Informatica • Fivetran • Azure Data Factory • AWS Glue • Collibra • Alation • 2 months ago

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Salary

≈ $16k – $42k per year (Estimated)

Location

Hybrid (Bengaluru, India)

Confirmed on the employer's own hiring board on Oct 26, 2026. First seen by Alion on Aug 10, 2026. Brillio scores B on the Alion truth index.

Overview

Company

Impact

Profile match

Brillio

Brillio is an American digital technology services company founded in 2014 that works on data engineering, cloud modernisation, customer experience platforms and applied artificial intelligence for large enterprises. Its clients are concentrated in banking and financial services, healthcare, technology and retail, and it positions itself around building and running digital products rather than the traditional labour-arbitrage outsourcing model. Headquartered in Edison, New Jersey with its largest delivery base in India, it is backed by Bain Capital and employs several thousand engineers across the United States, India, Mexico and Europe.

brillio.com • HQ: Edison, United States • Cloud Consulting & Migration • AI Consulting & Integration • Data Engineering & Migration Services • IT Consulting & Digital Transformation • 501-1000 employees • Est. 2014

Tech stack of Brillio

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

Specialization

Databricks Engineering: Lead Data Engineer

Job requirements

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 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

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