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.
- Solid 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.

📌 Data Architect - Python/SQL/ETL (India)
🏢 Growel Softech
📍 India