Job Title: Data / BI Architect
Location: Pontiac, MI
Duration: 1–2 Years
Overview:
We are seeking a Data / Business Intelligence (BI) Architect to play a critical role in shaping and executing enterprise-wide data strategies. This hybrid role blends data architecture, engineering, and business strategy to bridge the gap between technical data solutions and organizational objectives. The architect will design, develop, and maintain scalable and secure data platforms, ensuring that data assets are accessible, reliable, and optimized for advanced analytics and decision-making.
Key Responsibilities:
Stakeholder Collaboration: Partner with business and IT stakeholders to gather requirements, identify data sources, and translate business needs into technical solutions.
Data Architecture & Modeling: Design and implement scalable, secure, and efficient data solutions (data warehouses, lakes, and marts). Create conceptual, logical, and physical data models.
Tool & Platform Strategy: Evaluate and recommend technologies, including BI visualization tools, aligned with organizational needs.
ETL/ELT Pipeline Management: Build and maintain robust data pipelines and integrations to support data warehouse environments.
Data Catalog & Metadata Management: Develop and maintain an enterprise-wide data catalog, ensuring automated metadata ingestion, data dictionaries, and standardized tagging.
Data Governance & Discovery: Enforce governance policies, ensuring data quality, compliance, and security. Enable self-service discovery by organizing data assets effectively.
Performance Optimization: Monitor and optimize BI systems, pipelines, and platforms for reliability, scalability, and cost efficiency.
Technical Leadership: Provide mentorship and establish best practices in data management, architecture, and BI development across the organization.
Technical Environment (Experience in all not required):
Data Platforms: Data warehouse & lake concepts (e.g., AWS Redshift, Azure Synapse, Google BigQuery, Databricks, Snowflake, Informatica).
Databases: SQL and relational/non-relational (e.g., SQL Server, Oracle, PostgreSQL, MongoDB).
BI Tools: Power BI, Tableau, Business Objects, Crystal Reports, Looker.
ETL/ELT Tools: AWS Glue, Azure Data Factory, Google Cloud Dataflow, Fivetran, Talend, dbt.
Big Data Technologies: Hadoop, Spark, Kafka.
Programming & APIs: Python, R, XML, Scikit-learn, Keras.
Analytics & ML/DL Engines: TensorFlow, PyTorch, Apache Spark.
Modeling Tools: MS Visio, ER/Studio, PowerDesigner.
Source Systems: On-premises, cloud, and SaaS environments.