Data Architect

Role & responsibilities

1. Data Architecture & Engineering

• Design and implement scalable data architectures using Databricks.
• Develop end-to-end data models and pipelines to support analytics, machine learning, and real-time processing.
• Ensure best practices for data storage, transformation, governance, and security.
• Implement ETL/ELT workflows using Databricks, Python, PySpark, and SQL.
• Optimize Spark-based data processing for performance and cost efficiency.
• Work with Delta Lake, Unity Catalog, and Medallion Architecture for efficient data management.

2. Data Modeling & Database Optimization

• Design conceptual, logical, and physical data models for structured and semi-structured data.
• Develop and optimize dimensional models (Star Schema, Snowflake Schema) for analytics and reporting.
• Define metadata management, lineage tracking, and data cataloging strategies.
• Optimize data bricks performance with partitioning, indexing, and query tuning.
• Work with relational and NoSQL databases to build robust data solutions.

3. Automation, Problem-Solving & Governance

• Develop reusable Python/PySpark scripts for data ingestion, transformation, and validation.
• Automate workflows and integrate with cloud services (AWS, Azure, GCP).
• Implement orchestration solutions using Apache Airflow, Azure Data Factory, or similar tools.
• Troubleshoot data pipeline issues and implement proactive monitoring.
• Design and implement data quality frameworks to ensure accurate and reliable datasets.
• Apply best practices in data governance, security, and compliance.

4. Collaboration & Communication

• Work closely with data engineers, analysts, and business stakeholders to define and deliver solutions.
• Communicate technical concepts effectively to both technical and non-technical audiences.
• Lead architectural discussions, provide guidance, and mentor teams on data engineering best practices. 

Preferred candidate profile

• Minimum 6 years of experience in data engineering, data architecture, or related fields.
• Extensive hands-on experience with Databricks, Delta Lake, and Apache Spark is mandatory.
• Strong data modeling expertise, with experience in relational and dimensional modeling, is preferred.
• Deep understanding of SQL-based data processing, indexing, and optimization techniques.
• Proficiency in Python and PySpark for data engineering workflows and pipeline development.
• Solid understanding of cloud platforms, specifically AWS and Azure, is preferred.
• Hands-on experience with data governance, data security, and compliance best practices.
• Experience with metadata management, data governance, and lineage tracking.
• Strong logical thinking, problem-solving, and analytical abilities.
• Excellent communication and stakeholder management skills.
• Certifications in Databricks, Azure, or AWS are a plus.
• Basic understanding of CI/CD for data pipelines and infrastructure-as-code (Terraform, CloudFormation, etc.).
• Familiarity with reporting concepts and visualization tools (Power BI, Tableau, or Looker) is a plus.