Top Data Engineer Jobs in Buffalo, NY

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms. Analyze complex issues, mentor junior staff, maintain data governance and security standards, build client relationships, and translate business needs into technical solutions.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver managed data and analytics solutions, mentor junior team members, maintain high-quality standards, and build client relationships while aligning work to business context and governance requirements.

Design, build, and maintain data infrastructure, pipelines, integrations, and transformations using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality, implement security best practices, and collaborate with clients to deliver actionable insights using platforms like Databricks, AWS, Azure Data Factory, Airflow, and Hadoop.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, data warehouses and lakes. Ensure data governance, security, pipeline reliability and cost/performance optimization. Coach and manage team members, partner with stakeholders to translate business requirements, and evaluate/adopt cloud data technologies across AWS, Azure, and GCP.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, and build BI reports and dashboards using tools like Oracle BI, QlikView, and Spotfire. Collaborate with clients and teams to analyze data, design dimensional models (star/snowflake), improve data quality, and support regulated environments while learning and growing technical and client-facing skills.

Lead design and implementation of data architecture and cloud-based data platforms. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and coach diverse teams to deliver scalable data solutions.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Lead design and delivery of data engineering solutions for clients: build and maintain ETL pipelines and architectures, apply AWS/Redshift, DataStage, SQL Server and DB2, implement BI visualizations, coach teams, engage stakeholders, validate outcomes, and optimize managed-service performance under SLAs.

Lead data engineering projects, design and maintain ETL/ELT pipelines and architectures, implement analytics solutions using cloud and BI tools, mentor and manage teams, engage stakeholders to improve data usage, and ensure project delivery and technical standards.

Senior individual contributor who designs, builds, and evolves Azure-based enterprise cloud data platform capabilities. Guides architecture, standards, reusable components, automation, security, and enablement for analytics, ingestion, storage, processing, and operationalization while providing hands-on technical leadership and mentorship.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

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

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.