Job Description
Senior Architect Data Engineering
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.
=e 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 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