Databricks Data Engineer with DevOps Skills

PeopleNTech LLC

Alexandria, VA
30+ days ago

$75 Per Hour

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Skills

AWS Lambdaunmatched
Access Controlunmatched
Amazon Simple Storage Service (S3)unmatched
Amazon Web Services (AWS)unmatched
Apache Sparkunmatched
Application Programming Interface (API)unmatched
Architectural Servicesunmatched
Best Practicesunmatched
Business Intelligenceunmatched
Business Supportunmatched
Cachingunmatched
Cisco Unityunmatched
Cloud Computingunmatched
Coachingunmatched
Configuration Managementunmatched
Continuous Deployment/Deliveryunmatched
Continuous Integrationunmatched
Cost Controlunmatched
Cross-Functionalunmatched
Data Analysisunmatched
Data Managementunmatched
Data Partitioningunmatched
Data Processingunmatched
Data Setsunmatched
Data Storageunmatched
Data Warehousingunmatched
Database Extract Transform and Load (ETL)unmatched
DevOpsunmatched
Gitunmatched
GitHubunmatched
High Reliabilityunmatched
Identify Issuesunmatched
Machine Toolunmatched
Performance Tuning/Optimizationunmatched
Policy Developmentunmatched
Product Engineeringunmatched
Regulatory Complianceunmatched
Relational Databases (RDBMS)unmatched
Requirements Managementunmatched
SQL (Structured Query Language)unmatched
Scalable System Developmentunmatched
Source Code/Configuration Management (SCM)unmatched
Streaming Technologyunmatched
Technical Leadershipunmatched

Description

Job Title: Databricks Data Engineer with DevOps Skills
Location : Los Angeles CA (Hybrid)
Hire type : FTE / CTH
Rate : $75/hr
Salary : $130K
Indent : SF_OP_185860-2-1

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 AWS, while driving automated CI/CD and deployment practices. This role requires strong skills in PySpark, SQL, AWS 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 AWS
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 AWS.
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.

2. DevOps & CI/CD

Implement CI/CD pipelines for Databricks using Git, GitLab, GitHub Actions, or AWS-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.

3. 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.

4. 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.

5. 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:
- Delta Lake
- Unity Catalog
- Lakehouse Architecture
- Delta Live Pipelines
- Databricks Runtime
- Table Triggers
- Databricks Workflows
Proficiency in PySpark, Spark, and advanced SQL.
Expertise with AWS cloud services, including:
- S3
- IAM
- Glue / Glue Catalog
- Lambda
- Kinesis (optional but beneficial)
- Secrets Manager
Strong understanding of DevOps tools:
- Git / GitLab
- CI/CD pipelines
- 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

Numbers & Facts

Location

Alexandria, VA

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