Databricks Data Engineer with DevOps Skills

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_(phone number removed)-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