JOBS 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 data in scope is accessible, reliable & secure for analysis and decision-making.

QUALIFICATIONS

• 5+ years of experience as a Data / BI Architect
• 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: 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 incl. on-prem, cloud, & SaaS
• Has experience in architecting data solutions that can be used for descriptive, diagnostic, predictive & prescriptive analytic solutions.

• Stakeholder Collaboration: Work closely with business & IT stakeholders to gather requirements & 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 the 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 intuitively.
• 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.