Senior Technical Architect

o9 Solutions, Inc.•Dallas, TX

1d•Hybrid

About The Position

At o9, our mission is to be the Most Value-Creating Platform for enterprises by transforming decision-making through our AI-first approach. By integrating siloed planning capabilities and capturing millions—even billions—in value leakage, we help businesses plan smarter and faster. This not only enhances operational efficiency but also reduces waste, leading to better outcomes for both businesses and the planet. Global leaders like Google, PepsiCo, Walmart, T-Mobile, AB InBev, and Starbucks trust o9 to optimize their supply chains. Senior Technical Architect needed by o9 Solutions, Inc. in Dallas, TX [& various unanticipated locations throughout the U.S.; may telecommute] to Collaborate with cross-functional teams, including data engineers, data scientists, and business stakeholders to validate, synthesize and transform customer data for integrated business planning and analytics. Review and analyze the data provided by customers along with its technical/functional intent and interdependencies. Participate in the technical design, data requirements gathering, and making recommendations in case of inaccurate or missing data. Design and automate the data loading and insights generation process with intelligent checks and balances for sustained value delivery. Identify and implement performance optimization strategies to enhance data processing and analytics capabilities. Design solutions for the performance problem mechanism regarding various DB technologies within various platforms. Create and execute test plans, document issues and track progress at resolving issues. Provide technical leadership and guidance to development teams, ensuring adherence to architectural principles and best practices. Leverage machine learning for predictive modeling in data architecture performance and business planning. Implement automated data quality checks and anomaly detection using AI algorithms. Utilize Generative AI for synthesizing realistic, anonymized test data. Develop AI-driven resource allocation models to optimize cloud infrastructure costs based on anticipated data workloads. Integrate natural language processing (NLP) capabilities to analyze unstructured data sources for hidden business insights.

Requirements

Bachelor’s degree, or foreign equivalent degree in Computer Science, Software Engineering, Operations Research, Industrial Engineering, Engineering Management, Electrical Engineering, Electronics Engineering, Business Analytics, Engineering or Information Systems
6 years of progressive, post-baccalaureate experience in the job offered or 6 years of progressive, post-baccalaureate experience in a related occupation driving the SDLC with key emphasis on architecting, designing, and developing solutions using Big Data Technologies such as Hive
Performing ETL development and designing ETL solutions for clients
Building data ingress or egress pipelines, handling of huge volume of data and developing data transformation functions using languages such as Python, HQL, Spark
Utilizing big data technologies such as Spark, Scala, and Hadoop
Utilizing various Hadoop infrastructures such as Map Reduce, Hive, Sqoop, Zookeeper and Oozie
Integrating various data sources definitions such as Teradata, SAP ERP, SQL Server, Oracle, ODBC connectors and Flat Files through API or batch
Utilizing unix shell scripting
Utilizing PL/SQL to write Stored Procedures, Functions and Triggers
Utilizing Spark Context, Spark-SQL, Spark YARN to improve the performance and optimization of the existing algorithms in Hadoop
Designing and deploying containerized micro services

Responsibilities

Collaborate with cross-functional teams, including data engineers, data scientists, and business stakeholders to validate, synthesize and transform customer data for integrated business planning and analytics.
Review and analyze the data provided by customers along with its technical/functional intent and interdependencies.
Participate in the technical design, data requirements gathering, and making recommendations in case of inaccurate or missing data.
Design and automate the data loading and insights generation process with intelligent checks and balances for sustained value delivery.
Identify and implement performance optimization strategies to enhance data processing and analytics capabilities.
Design solutions for the performance problem mechanism regarding various DB technologies within various platforms.
Create and execute test plans, document issues and track progress at resolving issues.
Provide technical leadership and guidance to development teams, ensuring adherence to architectural principles and best practices.
Leverage machine learning for predictive modeling in data architecture performance and business planning.
Implement automated data quality checks and anomaly detection using AI algorithms.
Utilize Generative AI for synthesizing realistic, anonymized test data.
Develop AI-driven resource allocation models to optimize cloud infrastructure costs based on anticipated data workloads.
Integrate natural language processing (NLP) capabilities to analyze unstructured data sources for hidden business insights.