Job Description:Data Business Intelligence (BI) Architect role is a hybrid of data architecture, engineering & business strategy, bridging the gap between tech data solutions & business objectives. Designs, develops & maintains the overall data strategy ensuring the County data in scope is accessible, reliable & secure for analysis and decision-making. The right candidate has experience in architecting data solutions that can be used for descriptive, diagnostic, predictive & prescriptive analytic solutions.
Key responsibilities: Stakeholder Collaboration:- Work closely with business & IT stakeholders to gather req & translate business needs into tech specifications, including identification of data sources.
Data Arch Design & Data Modeling:- Architect & implement scalable, secure & efficient data solutions, including data warehouses, data lakes, and/or data marts. Design conceptual, logical & physical data models.
Tool and Platform Selection:- Evaluate, recommend & implement tools aligned with recommended architecture, including visualization tools aligned with business needs.
ETL/ELT Pipeline Mgt:- Design, develop & test data pipelines, integrations to source mgt & ETL / ELT processes to move data from various sources into the data warehouse.
Data Catalog & Metadata Mgt:- Design, create & maintain an enterprise-wide data catalog, automating metadata ingestion, establishing data dictionaries, and ensuring that all data assets are properly documented & tagged.
Data Governance and Discovery:- Enforce data governance policies through the data catalog, ensuring data quality, security & compliance. Enable self-service data discovery for users by curating & organizing data assets in an intuitive way.
Performance Optimization:- Monitor & optimize BI systems & data pipelines to ensure high performance, reliability & cost-effectiveness.
Technical Leadership:- Provide technical guidance & mentorship across the organization, establishing best practices for data mgt & BI development.
Environment:Role incl. defining data platform tech stack. Example tech below; not required to have experience in all.
Data Platforms:- DW & lake concepts incl. dimensional modeling & cloud services (S3, AWS Redshift, RDS, Azure Data Lake Storage, Synapse Analytics, BigQuery, Databricks, Snowflake, Informatica);
Databases:- SQL & relational/non-relational (SQL Server, Oracle, PostgreSQL, MongoDB);
BI Tools:- Power BI, Business Objects, Tableau, Crystal, Looker;
ETL/ELT:- Cloud native (AWS Glue, Azure Data Factory, Google Cloud Dataflow) & in-warehouse transform tools (Fivetran, Talend, dbt);
Big Data Tech:Programming/API: - Python, Keras, Scikit-learn, R, XML;
ML/DL/Analytic Engines:- TensorFlow, PyTorch, Trillium, Apache Spark;
Modeling Tools:- MS Visio, ER/Studio, PowerDesigner; Source systems incl. on-prem, cloud, & SaaS