Data Architect

Description:

Must have - The candidate must have experience in data warehouse models in addition to OLTP models. These models will be architected at the following layers: conceptual, logical, physical, subject area, and application.

Responsibilities:

1. The candidate must have experience in data warehouse models in addition to OLTP models. These models will be architected at the following layers: conceptual, logical, physical, subject area, and application.

2. Work collaboratively on design implementation with other architects, ensuring that key requirements are preserved, and potential gaps are understood and communicated to stakeholders as appropriate.

3. Ensure design governance across projects, applications, and infrastructure.

4. Create short-term tactical solutions to achieve long-term objectives and an overall data management roadmap.

5. Establish processes for governing the identification, collection, and use of corporate metadata; take steps to assure metadata accuracy and validity.

6. Establish methods and procedures for tracking data quality, completeness, redundancy, and improvement.

7. Ensure that data strategies and architectures are in regulatory compliance.

8. Oversee the mapping of data sources, data movement, interfaces, and analytics, with the goal of ensuring data quality.

9. Address data-related problems regarding systems integration, compatibility, and multiple platform integration.

10. Develop and implement key components as needed to create testing criteria that will guarantee the fidelity and performance of data architecture.

11. Document the data architecture and environment to maintain a current and accurate view of the larger data picture.

12. Identify and develop opportunities for data reuse, migration, or retirement.

13. Analyze and mine business data to identify patterns and correlations among the various data points.

14. Develop and promote data management methodologies and standards.

15. Develop integration process data flows and data mapping analyses.

16. Maintain code in GitHub, ensuring proper version control and adherence to coding standards.

17. Accurately and promptly log all work, including estimates and time spent, in JIRA.

Required qualifications:

1. 4-7 years overall experience in Data Integration, Data Architecture, Data Modeling, and implementation.

2. 4-7 years of full lifecycle data warehousing experience.

3. In depth hands-on experience designing, implementing, and troubleshooting ETL/ELT processes.

4. Knowledge of relational database designs and concepts including normalization and dimensional models.

5. Knowledge of state longitudinal data systems and preparing them for technological advancements and features.

6. Experience with Apache Airflow and creating DAGs to move data from source to target.

7. Experience with Python coding.

8. Develop system/integration test plans and scenarios for data loads and extracts.

9. Manage and monitor production and non-production ETL sessions.

10. Ability to coordinate and work with network and other system administration teams to isolate resource bottlenecks (bandwidth/network, CPU, memory, disk/storage).

11. Good knowledge of applicable data privacy practices and laws (FERPA).

12. Knowledge of different standards in the Education domain CEDS, ED-FI, SIF.

13. Familiarity with student academic performance data and education data concepts.

14. BI knowledge: 3-5 years in BI from ETL perspective.

15. Ability to observe steps and ascertain success of step or failure and root cause of step(s).

16. Direct experience in implementing enterprise data management processes, procedures, and decision support.

17. Experience in Dimensional Modeling.

18. Experience with Snowflake a plus.

19. Experience with modern data management in the cloud, preferably AWS (e.g.