Databricks Data Engineer
Job Title: Databricks Data Engineer Location : Los Angeles CA or New York, NY (Hybrid)Contract / FulltimeJob SummaryWe 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 AWSClear separation of compute vs. serving layersAbility to design low-latency data/API access strategies (beyond Spark-only patterns)Strong understanding of caching strategies for performance and cost optimizationData partitioning, storage optimization, and file layout strategyAbility to handle multi-terabyte structured or time-series datasetsSkill in requirement probing, identifying what matters architecturallyA player-coach mindset: hands-on engineering + technical leadershipKey Responsibilities1. Data Pipeline DevelopmentDesign, 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/CDImplement 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 SupportWork 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 & OptimizationTune 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 & SecurityImplement 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 SkillsStrong hands-on experience with Databricks, including:Delta LakeUnity CatalogLakehouse ArchitectureDelta Live PipelinesDatabricks RuntimeTable TriggersDatabricks WorkflowsProficiency in PySpark, Spark, and advanced SQL.Expertise with AWS cloud services, including:S3IAMGlue / Glue CatalogLambdaKinesis (optional but beneficial)Secrets ManagerStrong understanding of DevOps tools:Git / GitLabCI/CD pipelinesDatabricks Asset BundlesFamiliarity with Terraform is a plus.Experience with relational databases and data warehouse concepts.Preferred ExperienceKnowledge 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 / ProfessionalAWS Data Engineer or AWS Solutions Architect certification