Data engineer • irvine ca
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The average salary range is between $ 120,000 and $ 179,111 year , with the average salary hovering around $ 144,491 year .
The average salary range is between $ 105,570 and $ 172,017 year , with the average salary hovering around $ 130,000 year .
Location: Irvine, CA (Onsite)
Fulltime Permanent
Job Description
Senior Data Engineer
We are seeking a hands-on Senior Data Engineer to design, build, optimize, and support enterprise-scale data solutions on the Databricks Lakehouse Platform. The role will develop reliable batch and streaming pipelines, modernize legacy ETL workloads, implement governed data models, and deliver trusted data products for analytics, reporting, risk, regulatory, and investment-management use cases.
The ideal candidate has deep experience with Databricks, Apache Spark, PySpark, SQL, Python, dbt, Apache Airflow, Delta Lake, Unity Catalog, cloud storage, data quality, CI/CD, and production support.
Key Responsibilities
Data Engineering and Development
Design, build, test, deploy, and maintain scalable ETL and ELT pipelines using Databricks, PySpark, Spark SQL, Python, and SQL.
Develop reusable ingestion and transformation frameworks for structured, semi-structured, and streaming data.
Implement batch, incremental, change-data-capture, and streaming processing patterns.
Build and maintain Delta Lake tables using medallion architecture across Bronze, Silver, and Gold layers.
Develop dbt models, tests, macros, packages, documentation, and incremental processing patterns.
Create and maintain Apache Airflow DAGs and Databricks Workflows with dependency management, retries, alerting, and operational controls.
Integrate data from APIs, databases, files, event streams, and cloud data services.
Produce technical designs, mapping specifications, lineage documentation, deployment instructions, and operational runbooks.
Performance, Reliability, and Data Quality
Tune Spark workloads, joins, partitioning, file sizes, caching, cluster configurations, and query plans.
Apply Delta Lake optimization techniques, including compaction, data skipping, clustering, retention, and vacuum controls.
Implement automated data quality, reconciliation, schema validation, observability, and freshness checks.
Monitor pipeline health and resolve failures, performance degradation, data defects, and service-level breaches.
Perform root-cause analysis and implement durable preventive measures.
Support release readiness, production cutover, incident resolution, and ongoing platform operations.
Improve compute utilization and cost efficiency across batch and streaming workloads.
Governance, Security, and Delivery Practices
Apply Unity Catalog standards for catalogs, schemas, tables, views, lineage, classification, and controlled access.
Implement secure handling of credentials, secrets, personally identifiable information, and regulated data.
Contribute to CI/CD pipelines, automated testing, code-quality checks, and environment promotion.
Use Git-based development, peer reviews, branching standards, and release-management practices.
Collaborate with platform engineers to deploy data assets through Terraform and Databricks Asset Bundles where applicable.
Follow enterprise architecture, security, data-governance, and regulatory requirements.
Collaboration and Mentoring
Partner with architects, product owners, analysts, data scientists, governance teams, and business stakeholders.
Translate business requirements into scalable data models, pipelines, and technical work packages.
Conduct code reviews and enforce engineering, documentation, testing, and support standards.
Mentor junior and mid-level engineers and share reusable patterns and best practices.
Communicate delivery status, risks, dependencies, and technical trade-offs clearly.
Required Qualifications
Typically 7 10 years of data engineering, data warehousing, or distributed data-processing experience.
Strong hands-on experience with Databricks, Apache Spark, PySpark, Delta Lake, Python, and advanced SQL.
Experience building production-grade ETL and ELT pipelines for large datasets.
Experience with dbt Core or dbt Cloud, including models, macros, tests, documentation, and incremental processing.
Experience with Apache Airflow, Astronomer, Databricks Workflows, or comparable orchestration platforms.
Experience with Unity Catalog, data lineage, role-based access, and data-governance controls.
Experience with cloud data services on AWS, Azure, or Google Cloud.
Working knowledge of Git, CI/CD, automated testing, monitoring, and production-support practices.
Strong troubleshooting, communication, collaboration, and technical-documentation skills.
Preferred Qualifications
Experience in banking, financial services, insurance, asset management, risk, compliance, or another regulated industry.
Experience modernizing Hadoop, legacy data warehouses, or traditional ETL platforms.
Experience with Kafka, Structured Streaming, Auto Loader, Delta Live Tables, or Lakeflow Declarative Pipelines.
Familiarity with Terraform, Databricks Asset Bundles, cloud networking, IAM, secrets management, and infrastructure automation.
Databricks Data Engineer Associate or Professional certification.
Experience delivering data reconciliation, regulatory reporting, test automation, and audit-ready controls.
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