Databricks Data Specialist

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