Top Analysis Reporting Jobs in Buffalo, NY

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

Leads client measurement strategy, analytics audits, tracking implementations, QA, data modeling, attribution, CRO experimentation, privacy compliance, and migration analytics. Presents actionable findings to clients, partners across delivery teams, and helps grow the agency’s analytics practice through standardized processes, tooling, AI adoption, and sales support.

Designs, builds, and operates scalable data pipelines, warehouses, lakes, and integrations supporting analytics, products, and production AI/ML systems. Responsibilities include data modeling, schema design, validation, monitoring, orchestration, real-time processing, governance, and observability. The role contributes to architecture decisions, collaborates across analytics, AI, and product teams, communicates technical tradeoffs, and owns data engineering work end to end.

Designs and operates data infrastructure for a clinical trial platform. Builds pipelines that extract, normalize, validate, and link information from clinical documents and publications into Aurora and GraphDB. Develops embeddings, vector search, RAG workflows, data-serving APIs, lineage tracking, and regulatory audit trails. The role also requires AWS-based orchestration, clinical data quality controls, biomedical knowledge graph modeling, and deployment automation using infrastructure-as-code and containerization.

Own university data integrations from source discovery through validated, signed-off feeds. Build SQL queries and scheduled extracts, model data, support partner upgrades and migrations, translate institutional policies into accurate data feeds, and improve monitoring, documentation, and reusable integration patterns. Collaborate with registrars and database administrators while supporting a named set of university partners. The role requires deep experience with student information systems, including PeopleSoft, and occasional travel for partner onsite engagements.

Own the analytical function for a business unit across growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, experiments, and analyses; generate actionable insights; communicate recommendations to leadership; and drive execution through measurable outcomes. The role requires independent problem-solving, strong analytical skills, persuasive communication, and ownership of ambiguous, high-impact initiatives.

Develops data infrastructure, analytics solutions, prototypes, and enterprise data assets supporting data science and predictive modeling. Collaborates with data scientists, engineers, and business partners to define requirements, build and test solutions, support user acceptance, and deliver rapid data engineering work tracks. Uses SQL, Python, and Spark for agile data analysis and parallel processing. Also contributes to proof-of-concept designs, documentation, process improvement, and team mentoring.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations through execution. The role requires strong first-principles thinking, independent data work, persuasive communication, stakeholder influence, and ownership of ambiguous, high-impact initiatives.

Designs and maintains scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, business, and product teams, contributes to shared codebases, establishes development standards, and participates in production on-call and incident response.