Senior Architect-Data Engineer

About the role

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.

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.

Requirements

• Overall Experience
  • 15+ years of overall 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 complex 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 Kinesis.

Pay

$150,000 - $240,000 a year