Architect- Data Engineering

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.

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