Data Architect - Python/SQL/ETL

Key Responsibilities :

- Design and implement scalable, high-performance enterprise data architectures.

- Define data models, data flows, integration patterns, and architecture standards.

- Develop and review Python-based data engineering solutions, frameworks, and reusable components.

- Design and optimize ETL/ELT pipelines for large-scale data processing.

- Work with structured and unstructured data across data lakes, data warehouses, and modern data platforms.

- Define strategies for data ingestion, transformation, storage, processing, and consumption.

- Collaborate with data engineers, software engineers, BI teams, and business stakeholders.

- Evaluate existing data platforms and recommend improvements for scalability, performance, reliability, and cost optimization.

- Establish data architecture best practices, standards, governance, and security principles.

- Perform technical reviews and provide architectural guidance to development teams.

- Troubleshoot complex data pipeline, integration, and performance issues.

- Support cloud migration and modernization initiatives.

- Create architecture diagrams, technical documentation, and solution design documents.

- Mentor data engineering teams and provide technical leadership on complex projects.

Required Skills & Experience :

- 8+ years of experience in data engineering, data architecture, or a related technology role.

- Excellent understanding of data structures, algorithms, object-oriented programming, and Python frameworks/libraries.

- Strong experience designing ETL/ELT pipelines and data integration solutions.

- Expertise in data modeling, including conceptual, logical, and physical data models.

- Experience with relational and NoSQL databases such as :

a. PostgreSQL

b. SQL Server

c. MySQL

d. MongoDB

- Other enterprise database technologies

- Strong SQL skills and experience with query optimization and performance tuning.

- Experience with modern data warehouse and data lake architectures.

- Hands-on experience with cloud data platforms such as AWS, Azure, or GCP.

- Experience with distributed data processing technologies such as Apache Spark/PySpark is preferred.

- Experience with orchestration and workflow tools such as Apache Airflow, Azure Data Factory, AWS Glue, or equivalent.

- Knowledge of data governance, data quality, security, metadata management, and data lineage.

- Experience designing scalable and highly available data solutions.