Top Remote Analysis Reporting Jobs in Buffalo, NY

Lead analytics engineering for Revenue, Marketing, and Finance by building scalable data solutions, data models, and dashboards; provide training and consultation, support ad hoc data needs, and translate technical findings into executive-level insights to improve operational performance.

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.

Gather and document business and functional requirements, analyze processes, recommend improvements, produce BRDs/FRDs/user stories and process maps, support solution design, UAT, testing and deployment, produce reports/dashboards, monitor project progress, and support change management and stakeholder communication.

Senior leader who defines and drives enterprise data governance and enablement strategy, partners with executives and cross-functional teams, leads readiness and impact analyses, sponsors governance programs, directs communications and training, monitors adoption metrics, and oversees multi-year data initiatives to ensure regulatory and policy compliance.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Develop and maintain Power BI, Tableau, or comparable dashboards for the VA Zero Trust acceleration effort. Translate validated maturity and implementation data into scorecards, trend views, gap analyses, risk visualizations, and executive reports. Partner with data engineering teams to automate refreshes, ensure consistent metric definitions, validate source data, and document dashboard logic. Support enterprise-level and drill-down analysis across pillars, capabilities, and implementation waves.

Builds and maintains scalable data pipelines and cloud infrastructure supporting an AI biomedical research platform. Responsibilities include ingesting, harmonizing, validating, and documenting multimodal neurodegeneration datasets; managing cloud storage, compute, permissions, and costs; implementing de-identification and data governance workflows; supporting research data repositories and AI/ML teams; and maintaining data lineage, catalogs, and technical documentation. The role requires strong Python and SQL skills, software engineering discipline, cloud experience, and neurodegeneration research data expertise.

Design and architect robust ETL/data pipelines on cloud platforms. Build and automate data ingestion and preprocessing using Spark/Databricks, AWS Glue, Airflow. Implement warehousing architectures (EDW, DM, ODS, MOLAP/ROLAP), ensure SQL-based data quality, use version control, and deliver tested production pipelines while communicating with stakeholders.

Build and maintain product analytics dashboards, validate and QA event tracking, translate event data into analytics-ready datasets, run funnels/cohorts/retention analyses, support A/B testing, and surface actionable product insights to Product, Growth, and Data stakeholders.

Design, build, and maintain data pipelines, dashboards, and predictive models. Analyze large datasets to generate actionable insights, collaborate with stakeholders, ensure data quality and governance, and document/share analytics best practices to support Marketing, Product, Operations, and Workforce teams.

Analyze large datasets, build SQL queries and data pipelines, develop predictive and statistical models, create dashboards, ensure data quality/governance, and translate insights into actionable recommendations for Marketing, Product, Operations, and Workforce teams.

Analyze large, complex datasets to generate actionable insights; build SQL queries and data pipelines; develop predictive models (regression, clustering, forecasting); create and maintain dashboards; ensure data quality and governance; collaborate with stakeholders to translate findings into business recommendations and document work.

Build and maintain data infrastructure, integrate veterinary partner systems, develop dbt models and pipelines, provide analytics modeling, testing, reporting, and deliver actionable insights to customers and internal teams.