Data Architect jobs in Ohio

Position Summary

The Data Architect demonstrates mastery of skills and knowledge and is a mentor, strategist, thought leader, evangelist, champion, Plans and leads data engineering activities and is responsible for overseeing the design, development, deployment, and maintenance of scalable and robust data solutions. Develops and manages data integration pipelines connecting disparate data sources. Works closely with data architects, data scientists, analysts, and other stakeholders to support business needs in analytical and data solutions/projects. Collaborates with Infrastructure and DBA teams to ensure appropriate infrastructure is in place. Optimizes and streamlines data processing efforts to ensure data quality, security, privacy, on time delivery and compliance. Provides technical leadership, mentorship, reviews deliverables and provides feedback to the data engineering team.

Duties

• Data Architecture and Technical Infrastructure (30%): Defines, plans, designs and support implementation of enterprise data architectures and enterprise data platform. Plans and leads data engineering activities for strategic, large, and complex programs. Leads the selection and development of data engineering methods, tools, and techniques.
• SDLC Methodology & Project Management (15%): Plans technical transitions between development, testing, and production phases of solutions' lifecycle, and the facilitation of the change control, problem management, and communication processes.
• Innovation, Continuous Improvement & Optimization (15%): Develops organizational policies, standards, and guidelines for the development and secure operation of data services and products. Ensures adherence to technical strategies and architectures.
• Data Modelling / Designing Datasets (10%): Coordinates the application of analysis, design, and modelling techniques to establish, modify or maintain data structures and their associated components. Manages the iteration, review and maintenance of data requirements and data models.
• Partnership and Community Building (10%): Collaborates with other IT teams, business community, data scientists and other architects to meet business requirements.
• Data Pipeline/ETL (5%): Sets standards for data modelling and design tools and techniques, advises on their application, and ensures compliance. Defines and implements administration and control activities related to data warehouse planning and development and the establishment of policies and procedures pertaining to its management, security, maintenance, and utilization.
• Support & Operations (5%): Manages the investigation of enterprise data requirements based upon a detailed understanding of information requirements.
• Data Governance and Data Quality (5%): Ensures that data is reliable, secure, and timely. Implement Data privacy and best practices. Defines, designs and implements data quality assessment and improvement methodology, processes and solutions.
• End-User Support, Education and Enablement (3%): Plans, designs, develops and facilitates training and Data Literacy initiatives within the team and End user community. #LI-KS1

Duties - cont'd

Education Degree

Required Experience

• Bachelor’s Degree in Computer Science, IT or related field
• 7-10 years of development, maintenance, and enhancement of Data Pipelines (ETL/ELT) and processes with thorough knowledge of star/snowflake schemas using cloud technologies
• 7-10 years of developing complex SQL queries and SQL optimization
• 7-10 years of development experience must be full Life Cycle experience including business requirements gathering, data sourcing, testing/data reconciliation, and deployment within Business Intelligence/Data Warehousing Architecture.
• 5-7 years of designing and implementing data security
• 7-10 years of monitoring and optimizing data storage and data processing
• 2-3 delivering Data Solutions using Cloud Technologies

PREFERRED EXPERIENCE

• Advanced SQL skills and experience with relational databases and database design like Oracle and SQLSERVER.
• Significant experience working with cloud Data Warehouse and Data Lake solutions (e.g., Snowflake, Redshift, BigQuery, Azure Data Lake Storage, Amazon S3, etc.)
• Experience working with data ingestion tools such as Fivetran, stitch, or Matillion.
• Working knowledge of Cloud-based solutions (e.g., Azure, AWS and GCP).
• Experience building and deploying machine learning models in production.
• Strong proficiency in object-oriented languages: Python, Java, C , Scala.
• Strong proficiency in scripting languages like Bash.