Job Title: Databricks Data Engineer with DevOps
Location: Los Angeles CA (Hybrid)
Hire type: FTE / CTH
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 mind set: hands-on engineering + technical leadership
Key Responsibilities
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/nonstructured 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.
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 Cloud Formation.
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.
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.
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
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