Overview
On Site
Full Time
Skills
Data Processing
Workflow
Analytics
PostgreSQL
Data Quality
Continuous Improvement
Mentorship
Data Engineering
Apache Spark
Python
PySpark
SQL
Performance Tuning
Extract
Transform
Load
ELT
Scheduling
Orchestration
Optimization
Data Modeling
Modeling
Batch Processing
Real-time
Streaming
Apache Kafka
Data Architecture
Cloud Computing
Design Patterns
Analytical Skill
Problem Solving
Conflict Resolution
Communication
Collaboration
Continuous Integration
Continuous Delivery
DevOps
Git
Terraform
ARM
Data Governance
Microsoft Azure
Machine Learning (ML)
Business Intelligence
Databricks
Job Details
We are looking for an experienced Senior/Lead Data Engineer with 8+ years of expertise in designing and delivering scalable, high-performing data solutions on the Azure ecosystem. The ideal candidate will have deep hands-on experience with Databricks, Spark, modern data lakehouse architectures, data modelling, and both batch and real-time data processing. You will be responsible for driving end-to-end data engineering initiatives, influencing architectural decisions, and ensuring robust, high-quality data pipelines.
The Opportunity:
Architect, design, and implement scalable data platforms and pipelines on Azure and Databricks.
Build and optimize data ingestion, transformation, and processing workflows across batch and real-time data streams.
Work extensively with ADLS, Delta Lake, and Spark (Python) for large-scale data engineering.
Lead the development of complex ETL/ELT pipelines, ensuring high quality, reliability, and performance.
Design and implement data models, including conceptual, logical, and physical models for analytics and operational workloads.
Work with relational and lakehouse systems, including PostgreSQL and Delta Lake.
Define and enforce best practices in data governance, data quality, security, and architecture.
Collaborate with architects, data scientists, analysts, and business teams to translate requirements into technical solutions.
Troubleshoot production issues, optimize performance, and support continuous improvement of the data platform.
Mentor junior engineers and contribute to building engineering standards and reusable components.
What You Need:
8+ years of hands-on data engineering experience in enterprise environments.
Strong expertise in Azure services, especially Azure Databricks, Functions, and Azure Data Factory (preferred).
Advanced proficiency in Apache Spark with Python (PySpark).
Strong command of SQL, query optimization, and performance tuning.
Deep understanding of ETL/ELT methodologies, data pipelines, and scheduling/orchestration.
Hands-on experience with Delta Lake (ACID transactions, optimization, schema evolution).
Strong experience in data modelling (normalized, dimensional, lakehouse modelling).
Experience in both batch processing and real-time/streaming data (Kafka, Event Hub, or similar).
Solid understanding of data architecture principles, distributed systems, and cloud-native design patterns.
Ability to design end-to-end solutions, evaluate trade-offs, and recommend best-fit architectures.
Strong analytical, problem-solving, and communication skills.
Ability to collaborate with cross-functional teams and lead technical discussions.
Preferred Skills:
Experience with CI/CD tools such as Azure DevOps and Git.
Familiarity with IaC tools (Terraform, ARM).
Exposure to data governance and cataloging tools (Azure Purview).
Experience supporting machine learning or BI workloads on Databricks.
The Opportunity:
Architect, design, and implement scalable data platforms and pipelines on Azure and Databricks.
Build and optimize data ingestion, transformation, and processing workflows across batch and real-time data streams.
Work extensively with ADLS, Delta Lake, and Spark (Python) for large-scale data engineering.
Lead the development of complex ETL/ELT pipelines, ensuring high quality, reliability, and performance.
Design and implement data models, including conceptual, logical, and physical models for analytics and operational workloads.
Work with relational and lakehouse systems, including PostgreSQL and Delta Lake.
Define and enforce best practices in data governance, data quality, security, and architecture.
Collaborate with architects, data scientists, analysts, and business teams to translate requirements into technical solutions.
Troubleshoot production issues, optimize performance, and support continuous improvement of the data platform.
Mentor junior engineers and contribute to building engineering standards and reusable components.
What You Need:
8+ years of hands-on data engineering experience in enterprise environments.
Strong expertise in Azure services, especially Azure Databricks, Functions, and Azure Data Factory (preferred).
Advanced proficiency in Apache Spark with Python (PySpark).
Strong command of SQL, query optimization, and performance tuning.
Deep understanding of ETL/ELT methodologies, data pipelines, and scheduling/orchestration.
Hands-on experience with Delta Lake (ACID transactions, optimization, schema evolution).
Strong experience in data modelling (normalized, dimensional, lakehouse modelling).
Experience in both batch processing and real-time/streaming data (Kafka, Event Hub, or similar).
Solid understanding of data architecture principles, distributed systems, and cloud-native design patterns.
Ability to design end-to-end solutions, evaluate trade-offs, and recommend best-fit architectures.
Strong analytical, problem-solving, and communication skills.
Ability to collaborate with cross-functional teams and lead technical discussions.
Preferred Skills:
Experience with CI/CD tools such as Azure DevOps and Git.
Familiarity with IaC tools (Terraform, ARM).
Exposure to data governance and cataloging tools (Azure Purview).
Experience supporting machine learning or BI workloads on Databricks.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.