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Manager Data Engineering Jobs in Irvine, CA (NOW HIRING)

Essc

Director, Data Engineering

Irvine, CA

Leads the Data Engineering function within the Enterprise Data & Analytics department by designing ... Manages assigned budgets, vendor resources, and program-related expenses to support the effective ...

Sayva Solutions

Director, Data Engineering

Irvine, CA · On-site

$133K - $167K/yr

Responsibilities: • Lead the Data Engineering function, including Manager-level direct reports and a broader team of data engineering professionals. • Partner with executive leadership to define ...

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All JobsData Engineering JobsManager Data Engineering JobsManager Data Engineering Jobs in Irvine, CA

Manager Data Engineering information

See Irvine, CA salary details

$33.3K

$104.3K

$184.6K

How much do manager data engineering jobs pay per year?

As of Sep 8, 2026, the average yearly pay for manager data engineering in Irvine, CA is $104,274.00, according to ZipRecruiter salary data. Most workers in this role earn between $70,800.00 and $134,700.00 per year, depending on experience, location, and employer.

What are the roles and responsibilities of a manager data engineering?

A Manager Data Engineering oversees teams that design, build, and maintain data infrastructure and pipelines for organizations. They are responsible for ensuring the efficient flow and storage of data, implementing best practices in data management, and collaborating with stakeholders to meet business data needs. Additionally, they mentor and guide data engineers, manage project timelines, and ensure data security and quality standards are met. Their role often involves strategic planning to enable data-driven decision making across the company.

What are the key skills and qualifications needed to thrive as a manager data engineering?

To thrive as a Manager Data Engineering, you need expertise in data architecture, advanced analytics, and leadership, typically supported by a degree in computer science or a related field. Familiarity with big data tools (like Hadoop, Spark), data warehousing systems, cloud platforms (AWS, Azure), and certifications such as AWS Certified Data Analytics are highly valued. Strong communication, problem-solving, and team management skills help drive project success and foster collaboration. These skills ensure effective data solutions, alignment with business goals, and the ability to lead and grow high-performing engineering teams.

How does a manager data engineering typically collaborate with data scientists and business stakeholders?

A Manager of Data Engineering often serves as a bridge between technical teams and business stakeholders. They work closely with data scientists to ensure that data pipelines and infrastructure meet analytical needs, while also translating business requirements into actionable engineering solutions. Regular coordination meetings, clear documentation, and cross-functional projects are common, enabling seamless collaboration and alignment on goals. This role requires strong communication skills and the ability to balance technical priorities with business objectives.

What is the difference between Manager Data Engineering vs Data Engineer?

AspectManager Data EngineeringData Engineer
Required CredentialsBachelor's or Master's in CS, Data Science, or related; often leadership experienceBachelor's or higher in CS, IT, or related; technical certifications optional
Work EnvironmentTeam leadership, project management, strategic planningData pipeline development, coding, data modeling
Employer & Industry UsageTech companies, finance, healthcare, where data teams are commonData-focused roles across various industries

The main difference is that Manager Data Engineering oversees data teams and projects, focusing on strategy and leadership, while Data Engineers handle the technical implementation of data pipelines and infrastructure. Managers typically have more experience and leadership skills, whereas Data Engineers are more hands-on with coding and data architecture.

What are the most commonly searched types of Data Engineering jobs in Irvine, CA?

The most popular types of Data Engineering jobs in Irvine, CA are:

What are popular job titles related to Manager Data Engineering jobs in Irvine, CA?

For Manager Data Engineering jobs in Irvine, CA, the most frequently searched job titles are:

What job categories do people searching Manager Data Engineering jobs in Irvine, CA look for?

The top searched job categories for Manager Data Engineering jobs in Irvine, CA are:

What cities near Irvine, CA are hiring for Manager Data Engineering jobs?

Cities near Irvine, CA with the most Manager Data Engineering job openings:

Manager Data Engineering jobs near you
Infographic showing various Manager Data Engineering job openings in Irvine, CA as of September 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 100% In-person job distribution, with an average salary of $104,274 per year, or $50.1 per hour.

Senior Architect- Data Engineering

Tustin, CA • On-site

Judge Group, Inc.
Recruiting and Staffing Services • 5 - 10K employees
Learn more about Judge Group, Inc.external

$200K - $220K/yr

Other

Posted 18 days ago

Job description

Location: Tustin, CA Salary: $200,000.00 USD Annually - $220,000.00 USD Annually Description:
Senior Architect- Data Engineering
Irvine, CA
Fulltime
Role Overview
We are seeking a visionary and hands-on Senior Data Engineering Architect to design, scale, and optimize our enterprise data platform. In this role, you will define the blueprints for our data estate, leveraging a modern stack centered on Databricks, dbt, and Apache Airflow. You will bridge the gap between complex business strategy and technical implementation, ensuring our data pipelines are scalable, resilient, and cost-effective.
Key Responsibilities
Architecture & Platform Design
Design end-to-end lakehouse architectures on Databricks utilizing Delta Lake and Unity Catalog.
Establish robust governance, schema evolution, and fine-grained data security patterns.
Formulate standard frameworks for data modeling (e.g., Kimball dimensional modeling, Data Vault 2.0).
Optimize infrastructure for optimal price-to-performance across batch and streaming workloads.
Data Pipeline & Orchestration Engineering
Architect modular, reusable transformation frameworks using dbt Core/Cloud integrated with Databricks.
Standardize data processing patterns using PySpark, Delta Live Tables (DLT), and Spark SQL.
Build highly observable, dynamic orchestration workflows using Apache Airflow.
Design cross-DAG dependency models, custom providers, and robust error-handling mechanisms.
DataOps & Engineering Excellence
Drive DataOps maturity by implementing CI/CD pipelines via GitHub Actions, GitLab CI, or Azure DevOps.
Deploy infrastructure-as-code patterns using Terraform and Databricks Asset Bundles (DABs).
Embed automated data quality testing directly into the dbt and Airflow lifecycle.
Define service-level indicators (SLIs) and objectives (SLOs) for pipeline uptime and data freshness.
Leadership & Stakeholder Management
Serve as the principal technical authority and escalation point for data engineering teams.
Mentor senior and mid-level data engineers through code reviews and architectural workshops.
Collaborate with product managers, data scientists, and business leaders to solve data gaps.
Required Qualifications
Overall 15+ Years of experience
10+ years of total experience in data engineering, data warehousing, and distributed systems.
4+ years of dedicated experience architecting production environments within the modern data stack.
Technical Proficiencies
Databricks: Advanced mastery of Photon engine, Unity Catalog, Delta Lake optimization (Z-order, Liquid Clustering), and DLT.
dbt: Expert-level proficiency with compl ex macro development, custom materializations, and multi-project dbt mesh architectures.
Airflow: Deep understanding of Airflow scheduling, custom operators, dynamic task mapping, and infrastructure scaling.
Languages: Elite proficiency in Python (PySpark) and advanced SQL.
Cloud Infrastructure: Strong experience with at least one major cloud ecosystem provider: AWS, Azure, or Google Cloud Platform.
Soft Skills
Strong technical communication skills to distill complex infrastructure designs for non-technical stakeholders.
Natural ability to lead by influence and drive cross-functional engineering initiatives.
Preferred Qualifications
Official Databricks certifications (e.g., Databricks Certified Data Engineer Professional or Solutions Architect).
Active contributor to open-source data communities (dbt, Airflow, or Apache Spark).
Solid foundation in streaming data technologies like Apache Kafka or AWS
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Contact:
This job and many more are available through The Judge Group. Please apply with us today!

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