Top Analysis Reporting Jobs in Buffalo, NY

Support marketing analytics by analyzing digital channel performance, building and maintaining KPI dashboards, ensuring data accuracy, and collaborating with cross-functional teams to turn insights into actionable recommendations.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.

Own pre-sales funnel analytics from lead to closed-won for EverHealth products. Build funnel conversion and pipeline-quality reporting, ICP and win/loss analysis, cohort and TAM analyses, Salesforce dashboards, and actionable insights for Sales, Marketing, and RevOps. Use AI tools to accelerate reporting and support GTM planning, quota modeling, and competitive intelligence.

Leads enterprise business intelligence and analytics across multiple divisions and locations. Establishes governed KPI definitions, reporting architecture, financial and operational analytics, semantic models, data-quality practices, and self-service reporting. Partners with FP&A, IT, Finance, and Operations to align analytics with financial results, guide technical delivery, evaluate BI platforms, and support forecasting and performance improvement. Directs internal or external development teams and communicates actionable insights to executives and operating leaders.

Analyze clinical data completeness, reliability, and effectiveness across healthcare facilities, vendors, interoperability networks, and integration pathways. Reconcile claims with clinical records, identify missingness and latency, report ADT coverage, evaluate data technologies, recommend vendor configurations, and prioritize facilities for direct acquisition. Partner with product, engineering, and clinical teams to improve scalable, trustworthy data pipelines and support TEFCA and QHIN strategy.

Manage human capital analytics and workforce metrics, including hiring-process analysis, time-in-stage, time-to-hire, yield rates, benchmarking, trend analysis, and performance measurement. Develop dashboards, visualizations, executive briefings, analytical presentations, and reports using business intelligence tools. Communicate complex findings and actionable recommendations to senior leadership while supporting federal or large organizational clients.

Leads customer data engineering strategy, delivery, team development, and roadmapping. Oversees data-driven product improvements, digital programs, quality initiatives, budgets, cybersecurity remediation, and portfolio management. Manages, mentors, and grows data engineering teams, including contractor teams, while guiding customer data platforms, omnichannel identity resolution, complex data modeling, and personalization-focused data science capabilities. Partners across the organization to execute strategic initiatives and build team capability.

Designs, develops, and maintains Databricks-based ETL pipelines, data integration solutions, data warehouses, and reporting datasets. Builds batch and streaming architectures using Python, Spark, SQL, and Delta Lake; optimizes performance and costs; migrates legacy SSIS and SQL Server processes; maintains SSRS reports; implements data quality controls; and collaborates with business and technical teams supporting analytics and AI/ML workloads.

Analyze and tune sanctions, fraud, and AML models; evaluate performance, reduce false positives, develop features and datasets, automate analysis, troubleshoot data issues, and communicate findings to technical, compliance, regulatory, and senior management stakeholders.