Data Architect

A Data Architect is responsible for designing, developing, data and ETL work for the NX organization. The data engineer must ensure data is efficiently stored, accessed, and analyzed by leveraging advanced data modeling techniques, cloud technologies, and data governance practices while collaborating with cross-functional teams to meet business needs and drive data-driven decision-making.

Data Integration and Pipeline Development: Design and implement data pipelines for data ingestion, transformation, and loading (ETL/ELT) across various data sources. Ensure seamless integration of data from disparate systems and applications. Optimize data flow for performance and scalability.

Design and implement data pipelines for data ingestion, transformation, and loading (ETL/ELT) across various data sources.

Ensure seamless integration of data from disparate systems and applications.

Optimize data flow for performance and scalability.

Cloud Architecture and Implementation: Leverage cloud-based data warehousing and data lake solutions (AWS, Azure, GCP) for data storage and processing. Design and implement cloud-native data architectures for scalability and cost-efficiency.

Leverage cloud-based data warehousing and data lake solutions (AWS, Azure, GCP) for data storage and processing.

Design and implement cloud-native data architectures for scalability and cost-efficiency.

Collaboration and Leadership: Work closely with business stakeholders, data analysts, data scientists, and data engineers to understand data requirements and translate them into technical solutions.

Work closely with business stakeholders, data analysts, data scientists, and data engineers to understand data requirements and translate them into technical solutions.

Performance Monitoring and Optimization: Monitor data quality and performance metrics to identify and address data issues. Continuously optimize data pipelines and database structures for improved efficiency.

Monitor data quality and performance metrics to identify and address data issues.

Continuously optimize data pipelines and database structures for improved efficiency.

Required Skills and Experience:

Technical Skills: Deep understanding of data modeling concepts (dimensional, star schema, snowflake) Must worked in SQL Query Language for “6” years – “Please decide based on the salary need” Must have worked on Oracle SQL or any relational databases, stored procedures, materialized views. Must have worked on Azure ADLS Blobs, Azure data factory. Good to have knowledge in Python language. Good to have knowledge on Visualization tools like Power BI. Worked on cloud databases like redshift, synapse, snowflake, firebolt etc is a plus Familiarity with data quality tools and techniques

Deep understanding of data modeling concepts (dimensional, star schema, snowflake)

Must worked in SQL Query Language for “6” years – “Please decide based on the salary need”

Must have worked on Oracle SQL or any relational databases, stored procedures, materialized views.

Must have worked on Azure ADLS Blobs, Azure data factory.

Good to have knowledge in Python language.

Good to have knowledge on Visualization tools like Power BI.

Worked on cloud databases like redshift, synapse, snowflake, firebolt etc is a plus

Familiarity with data quality tools and techniques

Soft Skills: Strong communication and collaboration skills to work effectively with cross-functional teams Analytical and problem-solving abilities to identify and resolve complex data issues

Strong communication and collaboration skills to work effectively with cross-functional teams

Analytical and problem-solving abilities to identify and resolve complex data issues