Top Data Engineer Jobs in Pittsburgh, PA

Lead and manage Managed Data & Analytics engagements, build modern data ecosystems, convert insights into strategic opportunities, mentor teams, ensure project delivery, uphold standards, and drive cloud-scale data solutions.

Design and build data architecture, ETL/ELT pipelines, and scalable cloud data solutions. Analyze complex problems, mentor junior staff, uphold project standards, and develop client relationships while advancing technical and business domain knowledge.

Design, implement, and manage advanced data ecosystems and pipelines. Analyze complex problems, build scalable ETL/ELT and data warehouse solutions, maintain data governance, mentor junior staff, and build client relationships to deliver high-quality managed data and analytics services.

Design and build data infrastructure, pipelines, integrations, and transformations using cloud and big-data platforms. Develop data lakes/warehouses, ensure data quality, apply data modeling, and collaborate with clients to deliver analytics and secure, scalable solutions.

Lead design and implementation of data architecture, data models, ETL/ELT pipelines, and data platform solutions. Manage data warehouses/lakes, enforce governance and security, troubleshoot pipelines, coach team members, engage stakeholders to translate requirements, and recommend cloud and tooling improvements to enable data-driven decisions.

Build and maintain data pipelines and ETL processes, optimize SQL and data models, create BI reports/dashboards, apply data architecture patterns, support client engagements, and improve data quality in regulated environments while developing technical skills.

Lead design and implementation of data architecture, pipelines, and cloud-based solutions. Manage large projects, advise clients strategically, ensure data governance and security compliance, document data models/flows, and build high-performing inclusive teams while coaching and driving operational excellence.

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 data pipelines and architectures, apply AWS/Redshift and ETL tooling, implement BI visualizations, coach teams, engage stakeholders, apply systems thinking, validate outcomes, and manage managed-services SLAs to optimize business performance.

Lead data engineering projects and teams to design, build, and maintain scalable ETL/ELT pipelines, cloud analytics platforms, and BI/reporting solutions. Mentor staff, manage client relationships, ensure project delivery, and promote technology adoption and process improvement to drive data-driven business decisions.

Design, build, test, and maintain robust data pipelines and systems for the Machine Learning Department. Lead small projects, mentor junior staff, translate multidisciplinary research requirements into production-ready solutions, troubleshoot system performance and availability, document systems, and evaluate feasibility and specifications.

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.