Intellibee
Data Business Intelligence (BI) Architect role is a hybrid of data architecture, engineering, and business strategy, bridging the gap between tech data solutions and business objectives.
Designs, develops, and maintains the overall data strategy ensuring the County data in scope is accessible, reliable, and secure for analysis and decision-making.
The right candidate has experience in architecting data solutions that can be used for descriptive, diagnostic, predictive, and prescriptive analytic solutions.
KEY RESPONSIBILITIES
Stakeholder Collaboration:
Work closely with business and IT stakeholders to gather requirements and translate business needs into tech specifications, including identification of data sources. Data Architecture Design & Data Modeling:
Architect and implement scalable, secure, and efficient data solutions, including data warehouses, data lakes, and/or data marts. Design conceptual, logical, and physical data models. Tool and Platform Selection:
Evaluate, recommend, and implement tools aligned with the recommended architecture, including visualization tools aligned with business needs. ETL/ELT Pipeline Management:
Design, develop, and test data pipelines, integrations to source management, and ETL/ELT processes to move data from various sources into the data warehouse. Data Catalog & Metadata Management:
Design, create, and maintain an enterprise-wide data catalog, automating metadata ingestion, establishing data dictionaries, and ensuring proper documentation and tagging of data assets. Data Governance and Discovery:
Enforce data governance policies through the data catalog, ensuring data quality, security, and compliance. Enable self-service data discovery by organizing data assets intuitively. Performance Optimization:
Monitor and optimize BI systems and data pipelines to ensure high performance, reliability, and cost-effectiveness. Technical Leadership:
Provide technical guidance and mentorship, establishing best practices for data management and BI development. Define the data platform tech stack. Example technologies include, but are not limited to: Data Platforms:
Data Warehouse and lake concepts, dimensional modeling, cloud services (S3, AWS Redshift, RDS, Azure Data Lake Storage, Synapse Analytics, BigQuery, Databricks, Snowflake, Informatica) Databases:
SQL and relational/non-relational databases (SQL Server, Oracle, PostgreSQL, MongoDB) BI Tools:
Power BI, Business Objects, Tableau, Crystal Reports, Looker ETL/ELT:
Cloud-native tools (AWS Glue, Azure Data Factory, Google Cloud Dataflow) and in-warehouse transform tools (Fivetran, Talend, dbt) Big Data Technologies:
Hadoop, Spark, Kafka 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 include on-premises, cloud, and SaaS solutions.
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Stakeholder Collaboration:
Work closely with business and IT stakeholders to gather requirements and translate business needs into tech specifications, including identification of data sources. Data Architecture Design & Data Modeling:
Architect and implement scalable, secure, and efficient data solutions, including data warehouses, data lakes, and/or data marts. Design conceptual, logical, and physical data models. Tool and Platform Selection:
Evaluate, recommend, and implement tools aligned with the recommended architecture, including visualization tools aligned with business needs. ETL/ELT Pipeline Management:
Design, develop, and test data pipelines, integrations to source management, and ETL/ELT processes to move data from various sources into the data warehouse. Data Catalog & Metadata Management:
Design, create, and maintain an enterprise-wide data catalog, automating metadata ingestion, establishing data dictionaries, and ensuring proper documentation and tagging of data assets. Data Governance and Discovery:
Enforce data governance policies through the data catalog, ensuring data quality, security, and compliance. Enable self-service data discovery by organizing data assets intuitively. Performance Optimization:
Monitor and optimize BI systems and data pipelines to ensure high performance, reliability, and cost-effectiveness. Technical Leadership:
Provide technical guidance and mentorship, establishing best practices for data management and BI development. Define the data platform tech stack. Example technologies include, but are not limited to: Data Platforms:
Data Warehouse and lake concepts, dimensional modeling, cloud services (S3, AWS Redshift, RDS, Azure Data Lake Storage, Synapse Analytics, BigQuery, Databricks, Snowflake, Informatica) Databases:
SQL and relational/non-relational databases (SQL Server, Oracle, PostgreSQL, MongoDB) BI Tools:
Power BI, Business Objects, Tableau, Crystal Reports, Looker ETL/ELT:
Cloud-native tools (AWS Glue, Azure Data Factory, Google Cloud Dataflow) and in-warehouse transform tools (Fivetran, Talend, dbt) Big Data Technologies:
Hadoop, Spark, Kafka 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 include on-premises, cloud, and SaaS solutions.
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