Sr Spec, Data Architect

L3Harris Technologies•Salt Lake City, UT

About The Position

As a member of the Data Analytics team, the Sr. Specialist, Data Architect supports efforts to solve important business problems, help internal departments make informed decisions, and deliver analytical insights. The role works closely with business stakeholders and technical teams to translate business needs into effective data and analytics solutions, including analytics, AI/ML, and data initiatives. It involves understanding business requirements, identifying opportunities to apply data, analytics, and AI/ML technologies, and contributing to solutions that support business objectives. The position designs, develops, and maintains scalable data pipelines and solutions; collaborates with data scientists, analysts, stakeholders, and engineering teams; and supports effective data modeling, database design, data security, quality, and governance. The role also evaluates and tests solutions, keeps informed about AI/ML developments and industry practices, assesses new tools, and mentors junior data engineers.

Requirements

Bachelor’s degree and at least 6 years of relevant experience; or a graduate degree and at least 4 years of related experience; or, in lieu of a degree, at least 10 years of related experience.
Bachelor’s degree in Data Science, Computer Science, Mathematics, Engineering, or a related field.
At least 4 years of experience in data science, advanced analytics, data analysis, or a related role.
Strong proficiency in data engineering languages such as Python, SQL, Java, or Scala.
Proficiency with large-scale data processing engines such as Spark, Flink, or Snowflake.
Prior experience as a data engineer, software engineer, or similar technical data professional.
Hands-on experience with orchestration and pipeline tools such as Apache Airflow, Azure Data Factory, Prefect, FiveTran, or Mage.
Knowledge of ANSI SQL and data manipulation and query development.
Experience with cloud-based AI/ML platforms such as AWS Bedrock or Azure Foundry, and open-source AI/ML solutions.
Experience with Agile software development, including tools such as Jira and Confluence.
Strong communication skills, both written and verbal, and ability to collaborate across functions.
Software engineering skills including version control such as Git, continuous integration, and automated testing.
Strong Python and SQL skills for data ingestion, transformation, and pipelining.
Hands-on knowledge of batch and stream processing, including Kinesis and Kafka, for large-scale data pipelines.
Knowledge of architecture and cloud platforms, preferably AWS and Azure.
Familiarity with containerization technologies such as Docker and Kubernetes.
Experience with Apache Airflow (MWAA), AWS EKS, and AWS RDS Aurora (MySQL).
Experience with data observability tools such as DataHub, Monte Carlo, Dynatrace, or Great Expectations.
Experience building AWS and Azure infrastructure.
Proactive mindset and interest in continuous learning and improvement.

Nice To Haves

Experience in government, defense, or a related regulated sector.
Relevant certifications in data, analytics, cloud, or project delivery; AWS, Azure, or other relevant platform certifications.
Strong knowledge of AI/ML concepts and common models such as regression, classification, clustering, and deep learning.
Proven experience in requirements gathering, solution design, and project management.
Ability to convey technical concepts to non-technical audiences.
Strong problem-solving skills and a proactive approach to identifying and addressing business challenges.
Experience with programming languages and tools commonly used in AI/ML workflows, such as Python, R, and SQL.

Responsibilities

Evaluate and identify data, analytics, and AI/ML approaches that align with business requirements.
Contribute to the design of scalable, practical analytics solutions that support business goals.
Design, develop, and maintain scalable and reliable data pipelines using tools such as Airflow, Python, SQL, and Databricks DLT.
Collaborate with data scientists, analysts, stakeholders, and engineering teams to ensure data is accessible, consistent, and reliable and to integrate pipelines with application systems.
Ingest, transform, and store structured and unstructured data to meet business requirements.
Implement data modeling and database design for analytic and operational use cases.
Apply best practices in data security, data quality, and data governance.
Optimize and tune SQL queries and database performance for high-concurrency environments.
Use cloud platforms such as AWS and Azure to design and deploy scalable, cost-effective data solutions.
Mentor junior data engineers and support their professional growth.
Participate in validation, testing, and review of analytics and AI/ML solutions to ensure they meet business and performance requirements.
Support data quality, consistency, and governance practices within assigned solutions.
Stay informed about AI/ML technology developments and industry best practices.
Assess new tools and technologies for potential organizational use.
Promote a collaborative team environment and support junior engineers and data scientists.