Top Senior Analysis Reporting Jobs in Birmingham, AL

Lead the enterprise data platform strategy and architecture, driving Snowflake/dbt-based platform evolution, self-service Data Mesh and medallion models. Build AI-ready pipelines, RAG systems, and observability/cost frameworks while managing a central data team, supporting federated BI, and executing hands-on technical work.

Lead strategic vision and execution of GitLab's data platform and analytics capabilities, focusing on AI-enabled self-service analytics and data governance.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

Lead and build a product analytics function: define KPI framework, instrumentation, and self-serve analytics using Pendo and BI tools. Partner cross-functionally to measure product performance, surface insights, influence roadmap and embed AI-powered analytics into product lifecycle.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

The Senior Analyst will support the personalization algorithm by conducting analyses, testing features, managing projects, and collaborating with teams to improve food recommendations for customers.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Design, build, and maintain cloud-based ETL pipelines and data models for finance. Implement data security (RLS), semantic layers for Power BI, monitor data integrity, support analytics/AI initiatives, and collaborate with Finance, Accounting, and IT to deliver high-quality BI solutions.

Lead onboarding, mapping, and validation for a Customer Data Hub: write user stories and acceptance criteria, define mastering/transformation logic, perform data lineage and reconciliation, validate integrations and outcomes, support production incidents, create runbooks and documentation, and partner with product, engineering, QA, and governance teams to enable reliable customer data for applications and analytics.

Own and maintain dbt models, ELT pipelines, and warehouse architecture (Snowflake/Redshift/BigQuery). Ensure data quality, testing, documentation, lineage, and metric governance. Partner with Finance, Operations, and Engineering to deliver authoritative KPIs, support executive reporting, and drive analytics engineering standards and roadmap.