Senior Architect – Data Engineering

Centraprise; Irvine (CA); On-site; USD 180,000 - 240,000; Full time

Job summary

Centraprise in Irvine, CA is seeking a Senior Data Engineering Architect to design, scale, and optimize the enterprise data platform. You will drive the blueprints for the data estate using Databricks, dbt, and Airflow, bridging business strategy with technical implementation across pipelines and data products.

You will mentor engineers, enforce data quality, and collaborate with product and analytics teams to deliver scalable, cost‑effective solutions worldwide.

Qualifications

• 15+ years of experience in data engineering, data warehousing, and distributed systems.
• 10+ years in data engineering roles and related architecture responsibilities.
• 4+ years architecting production environments in the modern data stack.
• Strong Python (PySpark) and SQL programming capabilities.
• Deep expertise with Databricks Photon, Unity Catalog, Delta Lake, and DLT.
• Advanced dbt proficiency including macros, materializations, and multi‑project setups.
• Airflow scheduling, operators, dynamic task mapping, and scalable infra.
• Cloud experience with AWS, Azure, or GCP, plus IaC tooling like Terraform.

Responsibilities

• Design end‑to‑end lakehouse architectures on Databricks with Delta Lake and Unity Catalog.
• Establish governance, schema evolution, and fine‑grained data security patterns.
• Define standard data modeling frameworks (Kimball, Data Vault 2.0).
• Optimize infrastructure for price‑to‑performance across batch and streaming workloads.
• Build modular transformation frameworks using dbt Core/Cloud with Databricks.
• Standardize processing with PySpark, Delta Live Tables, and Spark SQL.
• Construct observable orchestration workflows with Apache Airflow.
• Model cross‑DAG dependencies, providers, and robust error handling.
• Drive DataOps maturity with CI/CD pipelines via GitHub Actions, GitLab CI, or Azure DevOps.
• Apply IaC patterns with Terraform and Databricks Asset Bundles (DABs).
• Embed automated data quality testing into dbt and Airflow lifecycles.
• Define SLIs/SLOs for uptime and data freshness; act as data engineering authority.
• Mentor senior and mid‑level engineers through code reviews and workshops.
• Collaborate with product managers, data scientists, and business leaders on data gaps.

Job description

Location: Irvine, CA

Duration: Full Time

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

• 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.
• 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.
• 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.
• 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.
• 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 GCP.

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