Top Data Engineer Jobs in Buffalo, NY

The Senior Data Engineer will design and maintain scalable cloud-native data pipelines, develop APIs, implement advanced analytics, manage AI workflows, and enhance data operations collaboratively.

Lead design and scaling of backend data infrastructure for Beacon's Datastore: build event-driven pipelines, data models, and APIs; draft RFCs; integrate services; improve operational robustness, documentation, and tooling to support clinical and scientific workflows.

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

Founding data engineer to design and build Payabli's data platform: architect lakehouse/warehouse, build batch and streaming pipelines, model canonical datasets, ensure data quality/observability, enforce access/masking/lineage for regulated financial data, and enable analytics/ML feature pipelines while establishing team standards and CI/CD.

The Senior Data Engineer will maintain and improve data infrastructure, design ETL processes, and create high-performance pipelines for analytics and AI initiatives using SQL and Python in cloud environments.

Build and maintain scalable ETL/data pipelines and internal frameworks to support ML and product teams. Drive architecture, observability, data quality, and engineering best practices across AI/ML workflows and prompt-engineering pipelines.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Design, build, and maintain dbt data models and scalable ELT pipelines; optimize performance-aware SQL on Snowflake; ensure observability, testing, and CI/CD; partner with product and engineering teams to meet analytic needs, mentor engineers, document business logic, and troubleshoot ingestion, transformation, and warehouse issues.

Design, build, and maintain scalable data pipelines, data models, ETL/ELT, and BI solutions. Collaborate with data scientists and stakeholders to improve data quality, accessibility, and PHI security. Develop and document workflows, optimize processing/storage, support data warehouse and visualization, and adopt AI tools to drive data-informed outcomes.

Design, build, deploy, and maintain large-scale analytics solutions and ETL pipelines. Implement real-time streaming and batch processing, optimize SQL and data flows, ensure data quality, contribute to dev-ops and QA, cross-train team members, and participate in community and best-practice activities.

Design, build, deploy and maintain large-scale analytics and ETL solutions. Manage data ingestion, real-time streaming and batch processing across data stores, tune SQL and data flows, ensure quality, performance and maintainability, participate in QA/devops, cross-train team members, and contribute to community and best practices.

Design, build, and optimize scalable cloud data pipelines and infrastructure. Maintain and refactor SQL and ETL processes, automate data workflows, ensure data quality/security, and troubleshoot complex issues. Provide technical leadership and mentor junior engineers while collaborating with stakeholders to translate requirements.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Own and build scalable ELT pipelines and event-driven ingestion in AWS, using Dagster for orchestration. Implement IaC (Terraform), data quality, lineage, monitoring, and troubleshoot cross-system issues while partnering with analytics and product teams to ensure downstream analytical correctness.

Design and implement data federation and lakehouse architectures, build scalable ETL/ELT pipelines with Python and Spark, optimize performance across federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, security, and access controls for analytics and AI teams.

Design, build, test, and maintain high-performance Python data applications and backend services. Lead technical design, review code, mentor junior engineers, integrate with databases and cloud services, optimize performance, and ensure security, scalability, and reliability.

Design, develop, maintain, and optimize enterprise Tableau dashboards and BI solutions. Integrate and cleanse data from Oracle/Sybase/Big Data, tune SQL, automate Tableau extracts, manage Tableau Server on Linux, support testing, UAT, deployments, and troubleshoot data integrity and performance issues.

Design, build, and maintain scalable ETL/ELT pipelines into Snowflake, enforce data quality, optimize data models and query performance for Power BI reporting, produce documentation, and troubleshoot data issues while collaborating with stakeholders.

Design, implement, and maintain PySpark-based data reconciliation solutions for financial systems. Build matching algorithms, integrate with rules engines, process large distributed datasets, identify and resolve discrepancies, and collaborate with analysts and architects to improve data quality and governance.

Design, implement, and maintain data quality processes using cloud platforms (Azure/AWS/GCP). Collaborate with stakeholders to define standards, perform data profiling and cleansing, document issues and remediation, generate quality metrics, and support cross-functional teams to improve data architectures and integration.

Design, develop, test, and deploy high-performance Spark/Scala data processing applications and ETL pipelines using the Cloudera Hadoop ecosystem. Optimize Spark and platform performance, ensure data integrity and security, collaborate with data scientists and analysts, troubleshoot issues, and implement version control and CI/CD for Spark applications.

Design, build, and optimize PySpark applications and ETL pipelines to process large-scale datasets from SQL/NoSQL sources, data lakes, and streaming platforms. Ensure data quality, error handling, performance tuning, and collaborate with analysts, scientists, and architects to deliver scalable data solutions.

Design, implement, and maintain PySpark applications to automate large-scale financial data reconciliations. Build transformation and matching algorithms, integrate with rules engines, analyze data gaps, and collaborate with analysts and architects to ensure data quality and system resilience.

Design and develop Big Data applications using Java, Spark, and MapReduce on Cloudera/Hadoop ecosystems. Work with Hive, Impala, YARN, Kafka, and large datasets; write tests (JUnit), perform data analysis, and script in Unix/Python. Lead and manage global technology teams and follow industry best practices.