Top Data & Analytics Jobs in Buffalo, NY

Build and maintain scalable data pipelines, warehouse models, metrics, and data quality systems for Airbnb’s AirCover programs. Reconcile external partner data, publish analytics-ready datasets, improve pipeline observability and reliability, and collaborate with engineering, product, finance, operations, analytics, and data science stakeholders. Contribute to data engineering standards and foundational systems supporting reporting, experimentation, claims operations, financial reconciliation, and expansion into new markets.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.

Conduct market research and competitive intelligence on U.S. higher education trends, learner behavior, program demand, and emerging opportunities. Analyze internal and external datasets, build and maintain Fabric and Power BI models and dashboards, identify meaningful trends, and translate complex findings into executive-ready presentations. Partner with marketing and product teams on strategic research, portfolio reviews, and new program proposals while managing multiple analytical projects and ensuring data accuracy.

Supports learning evaluation, reporting, and operational effectiveness across Talent Capability and Learning. Collects and interprets qualitative and quantitative data, develops dashboards and recurring reports, applies learning evaluation methodologies, and translates findings into recommendations. Partners with stakeholders and cross-functional analytics and technology teams to improve data integrity, learning systems, reporting processes, and organizational decision-making. The role also supports Qualtrics, Workday, Power BI, Brightspace, and other learning platforms.

Design, build, and maintain Power BI dashboards and data models using Snowflake and other cloud sources. Collaborate with data engineers/ETL teams, gather stakeholder requirements, implement DAX and Power Query transformations, ensure data governance and performance, and deliver analytics within Agile processes.

As a Lead Data Engineer, you'll optimize data workflows, manage data engineering projects, mentor junior engineers, and ensure robust data solutions meet business needs.

Develop and maintain reports, dashboards, KPIs, and executive insights using SQL, Power BI, and other BI tools. Reconcile operational and financial data, investigate anomalies, ensure reporting quality, and document data models, metadata, governance, and reporting inventories. The role also performs advanced data manipulation, visualization, data storytelling, and analytical recommendations. Fraud and disputes domain knowledge is preferred.

Partner with stakeholders to translate business needs into BI requirements, design and maintain BI solutions and semantic layers, analyze large payment datasets, enforce data governance and quality, deliver insights and reports, lead stakeholder meetings and training, mentor team members, and drive process improvements for data loading and analytics.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, and SQL. Supports SQL Server, SSIS, SSRS, and data warehousing environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, enables reporting and AI/ML workloads, and partners with business and IT teams to deliver technical data solutions.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Designs, builds, and maintains scalable batch and streaming data pipelines and datasets in Databricks using Python, Spark, SQL, and Delta Lake. Supports SQL Server, SSIS, and SSRS environments while improving performance, reliability, data quality, and scalability. Integrates enterprise data sources, applies data warehousing practices, and partners with business users, analysts, and IT teams to deliver reporting, analytics, and AI/ML solutions.

Designs, develops, and maintains Databricks ETL pipelines, data lake architectures, datasets, and data integrations supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains legacy systems, implements data quality controls, and supports data warehousing. Maintains SSRS reports and collaborates with business users, analysts, and IT teams to deliver reporting solutions. Documents work, communicates technical information, and contributes to AI-assisted development practices.

Build scalable Tableau dashboards and reporting systems using operational data from Salesforce, AWS, SQL databases, APIs, and vendor platforms. Analyze large datasets, resolve data-quality issues, identify performance drivers and emerging risks, and translate findings into actionable recommendations. Prepare business-review insights for senior leaders, standardize metrics, improve reporting efficiency, and partner cross-functionally to support customer experience and operational decisions.

Lead business analytics strategy by developing dashboards, scorecards, KPIs, automated reporting, and performance measurement frameworks. Analyze sales and operational data to identify trends, risks, and opportunities, translating findings into actionable recommendations. Partner with Sales, Operations, Finance, IT, and leadership on forecasting, business cases, analytics modernization, governance, and process improvement. Communicate insights through reporting and data storytelling while mentoring analysts and leading cross-functional initiatives.

Lead enterprise and domain data modeling across conceptual, canonical, logical, physical, and business models. Translate business concepts into scalable data structures, facilitate stakeholder workshops, establish business glossaries, and align models with governance, metadata, MDM, analytics, AI, reporting, and data stewardship initiatives. Support engineering teams implementing cloud and lakehouse solutions while serving as a domain architecture lead for durable, flexible modern data platforms.