E-Solutions
Databricks Lead/architect with DevOps Skills
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Databricks Lead/Architect with DevOps skills, offering a remote contract. Key skills include Databricks, PySpark, SQL, Azure, and CI/CD. Experience in designing Lakehouse architectures and handling multi-terabyte datasets is essential.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
-
🗓️ - Date
April 25, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Remote
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#Scala #Security #API (Application Programming Interface) #Deployment #Azure cloud #Lambda (AWS Lambda) #GIT #Observability #Strategy #Storage #Data Warehouse #Data Engineering #Spark SQL #Delta Lake #Compliance #AWS (Amazon Web Services) #PySpark #Leadership #IAM (Identity and Access Management) #Logging #Terraform #Data Analysis #Data Processing #Monitoring #Version Control #DevOps #GitHub #Azure #SQL (Structured Query Language) #Data Governance #"ACID (Atomicity #Consistency #Isolation #Durability)" #GitLab #Cloud #Data Pipeline #Databricks #Spark (Apache Spark) #BI (Business Intelligence) #Databases #S3 (Amazon Simple Storage Service) #Triggers #Libraries #"ETL (Extract #Transform #Load)" #Datasets
Role description
Job Title: Databricks Lead/architect with DevOps Skills
Locationn - Remote
Job Summary
We are looking for an experienced Databricks Data Engineer with strong DevOps expertise to join our data engineering team. The ideal candidate will design, build, and optimize large-scale pipelines on the Databricks Lakehouse Platform on Azure, while driving automated CI/CD and deployment practices. This role requires strong skills in PySpark, SQL, Azure cloud services, and modern DevOps tooling. You will collaborate closely with cross-functional teams to deliver scalable, secure, and high-performance data solutions.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Must Demonstrate (Critical Skills & Architectural Competencies)
• Designing and implementing Databricks-based Lakehouse architectures on Azure
• Clear separation of compute vs. serving layers
• Ability to design low-latency data/API access strategies (beyond Spark-only patterns)
• Strong understanding of caching strategies for performance and cost optimization
• Data partitioning, storage optimization, and file layout strategy
• Ability to handle multi-terabyte structured or time-series datasets
• Skill in requirement probing, identifying what matters architecturally
• A player-coach mindset: hands-on engineering + technical leadership
Key Responsibilities
1. Data Pipeline Development
• Design, build, and maintain scalable ETL/ELT pipelines using Databricks on Azure.
• Develop high-performance data processing workflows using PySpark/Spark and SQL.
• Integrate data from Amazon S3, relational databases, and semi/non structured sources.
• Implement Delta Lake best practices including schema evolution, ACID, OPTIMIZE, ZORDER, partitioning, and file-size tuning.
• Ensure architectures support high-volume, multi-terabyte workloads.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. DevOps & CI/CD
• Implement CI/CD pipelines for Databricks using Git, GitLab, GitHub Actions, or Azure -native tools.
• Build and manage automated deployments using Databricks Asset Bundles.
• Manage version control for notebooks, workflows, libraries, and environment configuration.
• Automate cluster policies, job creation, environment provisioning, and configuration management.
• Support infrastructure-as-code via Terraform (preferred) or CloudFormation.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. Collaboration & Business Support
• Work with data analysts and BI teams to prepare curated datasets for reporting and analytics.
• Collaborate closely with product owners, engineering teams, and business partners to translate requirements into scalable implementations.
• Document data flows, technical architecture, and DevOps/deployment workflows.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. Performance & Optimization
• Tune Spark clusters, workflows, and queries for cost efficiency and compute performance.
• Monitor pipelines, troubleshoot failures, and maintain high reliability.
• Implement logging, monitoring, and observability across workflows and jobs.
• Apply caching strategies and workload optimization techniques to support low-latency consumption patterns.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. Governance & Security
• Implement and maintain data governance using Unity Catalog.
• Enforce access controls, security policies, and data compliance requirements.
• Ensure lineage, quality checks, and auditability across data flows.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Technical Skills
• Strong hands-on experience with Databricks, including:
o Delta Lake
o Unity Catalog
o Lakehouse Architecture
o Delta Live Pipelines
o Databricks Runtime
o Table Triggers
o Databricks Workflows
• Proficiency in PySpark, Spark, and advanced SQL.
• Expertise with AWS cloud services, including:
o S3
o IAM
o Glue / Glue Catalog
o Lambda
o Kinesis (optional but beneficial)
o Secrets Manager
• Strong understanding of DevOps tools:
o Git / GitLab
o CI/CD pipelines
o Databricks Asset Bundles
• Familiarity with Terraform is a plus.
• Experience with relational databases and data warehouse concepts.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Preferred Experience
• Knowledge of streaming technologies like Structured Streaming/Spark Streaming.
• Experience building real-time or near real-time pipelines.
• Exposure to advanced Databricks runtime configurations and performance tuning.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Certifications (Optional)
• Databricks Certified Data Engineer Associate / Professional
• AWS Data Engineer or AWS Solutions Architect certification
Job Title: Databricks Lead/architect with DevOps Skills
Locationn - Remote
Job Summary
We are looking for an experienced Databricks Data Engineer with strong DevOps expertise to join our data engineering team. The ideal candidate will design, build, and optimize large-scale pipelines on the Databricks Lakehouse Platform on Azure, while driving automated CI/CD and deployment practices. This role requires strong skills in PySpark, SQL, Azure cloud services, and modern DevOps tooling. You will collaborate closely with cross-functional teams to deliver scalable, secure, and high-performance data solutions.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Must Demonstrate (Critical Skills & Architectural Competencies)
• Designing and implementing Databricks-based Lakehouse architectures on Azure
• Clear separation of compute vs. serving layers
• Ability to design low-latency data/API access strategies (beyond Spark-only patterns)
• Strong understanding of caching strategies for performance and cost optimization
• Data partitioning, storage optimization, and file layout strategy
• Ability to handle multi-terabyte structured or time-series datasets
• Skill in requirement probing, identifying what matters architecturally
• A player-coach mindset: hands-on engineering + technical leadership
Key Responsibilities
1. Data Pipeline Development
• Design, build, and maintain scalable ETL/ELT pipelines using Databricks on Azure.
• Develop high-performance data processing workflows using PySpark/Spark and SQL.
• Integrate data from Amazon S3, relational databases, and semi/non structured sources.
• Implement Delta Lake best practices including schema evolution, ACID, OPTIMIZE, ZORDER, partitioning, and file-size tuning.
• Ensure architectures support high-volume, multi-terabyte workloads.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. DevOps & CI/CD
• Implement CI/CD pipelines for Databricks using Git, GitLab, GitHub Actions, or Azure -native tools.
• Build and manage automated deployments using Databricks Asset Bundles.
• Manage version control for notebooks, workflows, libraries, and environment configuration.
• Automate cluster policies, job creation, environment provisioning, and configuration management.
• Support infrastructure-as-code via Terraform (preferred) or CloudFormation.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. Collaboration & Business Support
• Work with data analysts and BI teams to prepare curated datasets for reporting and analytics.
• Collaborate closely with product owners, engineering teams, and business partners to translate requirements into scalable implementations.
• Document data flows, technical architecture, and DevOps/deployment workflows.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. Performance & Optimization
• Tune Spark clusters, workflows, and queries for cost efficiency and compute performance.
• Monitor pipelines, troubleshoot failures, and maintain high reliability.
• Implement logging, monitoring, and observability across workflows and jobs.
• Apply caching strategies and workload optimization techniques to support low-latency consumption patterns.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
1. Governance & Security
• Implement and maintain data governance using Unity Catalog.
• Enforce access controls, security policies, and data compliance requirements.
• Ensure lineage, quality checks, and auditability across data flows.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Technical Skills
• Strong hands-on experience with Databricks, including:
o Delta Lake
o Unity Catalog
o Lakehouse Architecture
o Delta Live Pipelines
o Databricks Runtime
o Table Triggers
o Databricks Workflows
• Proficiency in PySpark, Spark, and advanced SQL.
• Expertise with AWS cloud services, including:
o S3
o IAM
o Glue / Glue Catalog
o Lambda
o Kinesis (optional but beneficial)
o Secrets Manager
• Strong understanding of DevOps tools:
o Git / GitLab
o CI/CD pipelines
o Databricks Asset Bundles
• Familiarity with Terraform is a plus.
• Experience with relational databases and data warehouse concepts.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Preferred Experience
• Knowledge of streaming technologies like Structured Streaming/Spark Streaming.
• Experience building real-time or near real-time pipelines.
• Exposure to advanced Databricks runtime configurations and performance tuning.
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Certifications (Optional)
• Databricks Certified Data Engineer Associate / Professional
• AWS Data Engineer or AWS Solutions Architect certification
