Sr Spec, Data Architect

L3Harris•Salt Lake, UT

About The Position

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

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; strong Python and SQL skills for data ingestion, transformation, and pipelining.
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 and open-source AI/ML solutions.
Experience with Agile software development tools and practices, such as Jira and Confluence.
Solid software engineering skills, including version control such as Git, continuous integration, and automated testing.
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; experience building AWS and Azure infrastructure.
Familiarity with containerization technologies such as Docker and Kubernetes.
Experience with Apache Airflow (MWAA), AWS EKS, AWS RDS Aurora (MySQL), and data observability tools such as DataHub, Monte Carlo, Dynatrace, or Great Expectations.
Strong written and verbal communication skills, including the ability to communicate technical concepts to non-technical audiences.
Strong problem-solving skills, a proactive mindset, and a commitment to continuous learning and improvement.
Desire to be a team player and contribute to a collaborative environment.

Nice To Haves

Experience in government, defense, or a related regulated sector.
Relevant certifications in data, analytics, cloud, or project delivery.
Certifications in AWS, Azure, or other relevant platforms.
Strong knowledge of AI/ML concepts and common models, including regression, classification, clustering, and deep learning.
Proven experience in requirements gathering, solution design, and project management.
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, and other stakeholders to ensure data is accessible, consistent, and reliable.
Ingest, transform, and store structured and unstructured data to meet business requirements.
Implement data modeling and database designs for analytical 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.
Collaborate with software engineering and analytics engineering teams to integrate data pipelines with application systems.
Use AWS and Azure to design and deploy scalable, cost-effective data solutions.
Mentor junior data engineers and provide guidance to promote skill growth within the team.
Participate in validation, testing, and review of analytics and AI/ML solutions against business and performance requirements.
Support data quality, consistency, and governance practices within assigned solutions.
Stay informed about developments in AI/ML technologies and industry best practices.
Assess new tools and technologies for potential organizational use.
Help build a collaborative team environment and support junior engineers and data scientists.