Overview
Skills
Job Details
Job Title: Data / BI Architect
Location: Pontiac, MI
Duration: 1 2 Years
Position Overview
We are seeking a Data Business Intelligence (BI) Architect with strong expertise in data architecture, engineering, and business strategy. This role bridges the gap between technical data solutions and business objectives, ensuring data is accessible, reliable, and secure to support analytics and decision-making.
The ideal candidate has experience architecting data solutions to support descriptive, diagnostic, predictive, and prescriptive analytics.
Key Responsibilities
Stakeholder Collaboration: Partner with business and IT stakeholders to gather requirements and translate business needs into technical specifications, including identification of data sources.
Data Architecture & Modeling: Architect and implement scalable, secure, and efficient data solutions, including data warehouses, data lakes, and data marts. Design conceptual, logical, and physical data models.
Tool & Platform Selection: Evaluate, recommend, and implement BI and data tools aligned with organizational needs.
ETL/ELT Pipeline Management: Design, develop, and test data pipelines and integrations; manage ETL/ELT processes for ingesting data into the warehouse.
Data Catalog & Metadata Management: Create and maintain an enterprise-wide data catalog, automate metadata ingestion, establish data dictionaries, and ensure proper documentation of all data assets.
Data Governance & Discovery: Enforce governance policies to ensure data quality, security, and compliance. Enable self-service data discovery by organizing data assets in an intuitive manner.
Performance Optimization: Monitor and optimize BI systems and data pipelines for performance, reliability, and cost-effectiveness.
Technical Leadership: Provide guidance, mentorship, and establish best practices for data management and BI development.
Technical Environment
This role involves defining and influencing the data platform technology stack. Example technologies include (experience in all not required):
Data Platforms: Data warehouse & lake concepts (dimensional modeling, S3, AWS Redshift, RDS, Azure Data Lake, Synapse Analytics, BigQuery, Databricks, Snowflake, Informatica)
Databases: SQL Server, Oracle, PostgreSQL, MongoDB
BI Tools: Power BI, Business Objects, Tableau, Crystal, Looker
ETL/ELT: AWS Glue, Azure Data Factory, Google Cloud Dataflow, Fivetran, Talend, dbt
Big Data: Hadoop, Spark, Kafka
Programming & APIs: Python, R, XML, Keras, Scikit-learn
ML/DL & Analytics Engines: TensorFlow, PyTorch, Trillium, Apache Spark
Modeling Tools: MS Visio, ER/Studio, PowerDesigner
Source Systems: On-prem, cloud, SaaS