Senior Architect-Data Engineer

Job Description

Job Description

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