Top Data Engineer Jobs

As a Data Engineer at PNC, you will design, build, and maintain robust OLTP databases, ensuring high availability and strong performance while collaborating with cross-functional teams to manage data integrity and security.

Design, develop, and maintain scalable batch and streaming data pipelines using Python, PySpark, SQL, Databricks, Kafka, and Azure services. Provide production support, incident resolution, vulnerability remediation, platform upgrades, and reliability improvements. Optimize data models and workloads, implement automation and AI-enabled solutions, support CI/CD and monitoring, and ensure security, governance, and healthcare privacy compliance. Collaborate with business and technology partners on enterprise data solutions.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

As a Senior Lead Data Engineer at Capital One, you'll lead a development team, utilizing various big data and cloud technologies to deliver impactful solutions. Responsibilities include collaborating with Agile teams, mentoring, and ensuring optimal code performance.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

As a Lead Data Engineer, you'll lead the design and implementation of scalable data platforms, build distributed streaming data pipelines, and collaborate on technical solutions using big data technologies and agile methodologies.

Design, build, and maintain optimized, highly available data pipelines and the BMG data warehouse. Lead medium-to-complex data engineering initiatives, perform solution design, code reviews, QA, data integration, and collaborate across teams. Support cloud migration and ensure compliance with data policies for reporting and analytics.

Design, build, and maintain optimized, highly available data pipelines and the BMG Data Warehouse. Lead medium-to-complex data integration, data modeling, code reviews, QA, and cloud migration efforts. Collaborate with business partners to translate requirements into solutions, resolve technical issues, and support analytics and reporting needs while adhering to policies and compliance.

Summer internship focused on building scalable data architectures and pipelines, automating data processes, improving data quality frameworks, and supporting cloud infrastructure. Work includes AWS automation, data warehousing, workflow optimization, platform integration, and operationalizing tools to process structured and unstructured data while leveraging analytics, AI/ML, and standardized pipelines.

Design, build, and maintain scalable data solutions for enterprise counterparty credit risk. Lead and deliver moderately complex technical initiatives, collaborate with risk and business stakeholders, develop code/tests/documentation, support reporting and dashboards, and mentor junior staff.

Lead design, development, and support of scalable data solutions for counterparty credit risk. Build and maintain data pipelines, reports, and dashboards; collaborate with business stakeholders; resolve technical challenges; lead projects and mentor junior staff; ensure compliance with risk and regulatory requirements.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.

Build and maintain high-performance ETL/ELT pipelines and data schemas to connect engineering and operations data. Develop Protobuf models and gRPC microservices, administer and optimize Postgres databases, deploy data tools in Docker/Kubernetes, and implement automated data quality tests to ensure reliable, production-ready data for users.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

The Senior Data Engineer will design and maintain data pipelines, build reusable data sets, validate data integrity, and ensure user privacy and security. This role requires strong expertise in SQL and Python, and familiarity with AWS and various data technologies.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

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.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Build and maintain internal data application tools and frameworks for a centralized cloud data lake. Collaborate with cross-functional teams and Data Engineers to add features and maintain Go-based data pipelines. Improve platform architecture for scalability, observability, and reliability while sharing knowledge and mentoring teammates.

Lead and mentor a data engineering team to design, build, test, and deploy cloud-based, AI-enhanced risk management data solutions. Collaborate across Agile teams and product managers, implement big-data and streaming systems, perform unit tests and code reviews, and drive technology adoption and performance tuning.

Design, develop, test, implement, and support cloud-based data solutions and pipelines. Collaborate across Agile teams on distributed systems, microservices, ML integration, streaming, NoSQL/RDBMS, and data warehousing (Redshift/Snowflake). Mentor engineers, perform unit tests and code reviews, and optimize performance.

The Data Engineer III will design, build, and optimize data pipelines and backend systems, integrating with SAP and supporting analytics applications. Responsibilities include developing ETL workflows, backend applications, and ensuring data quality.

Lead design and build scalable, low-latency streaming data platforms and distributed data pipelines. Mentor engineers, define data architecture and best practices, collaborate across agile teams, implement cloud-based analytics stores, and ensure CI/CD, resiliency, and security for production streaming and batch data systems.

Designs and operates scalable GCP data pipelines, reusable engineering frameworks, feature stores, orchestration workflows, and governed data models supporting AI and machine learning. Builds Python, SQL, and Beam transformation libraries; ensures workload cost, performance, reliability, quality, lineage, and observability; and partners with data scientists migrating models to GCP. The role also documents best practices, provides enablement, and supports production systems during off-hours.