Top Data & Analytics Jobs

Design, build, and maintain data architectures and pipelines to ensure data quality, lineage, and accessibility. Implement ingestion frameworks and transformations across cloud and on-prem platforms, enable data consumption (APIs, views, extracts), monitor data observability, and collaborate cross-functionally to meet privacy and business requirements. Work may include nights/weekends and requires independent technical judgment.

Join BlackRock's Advanced Data Analytics team to deliver research and data solutions for structured products, leveraging data technologies and statistical methodologies.

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

As a Manager in Managed Services, you'll lead data ecosystems projects, mentor juniors, manage client accounts, and deliver data strategies to optimize operations and drive insights for clients.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.

Design, build, and maintain data pipelines, ingestion frameworks, and warehouses/lakes (Snowflake, Databricks). Ensure data quality, lineage, and transformation; support analytics and ML; create APIs and views for consumption; participate in code reviews and data governance. Work may require nights/weekends.

The Principal Data Engineer at NBCUniversal leads the development of data applications, manages data pipelines, and collaborates with teams while coaching junior engineers. Responsibilities include designing scalable data solutions and integrating AI technologies.

Design, build, and optimize scalable Azure data pipelines and AI platforms using Databricks, Spark, and Python. Operationalize ML models, maintain feature stores and real-time streaming, ensure data quality/governance/security, implement CI/CD, provide production support, and develop reusable frameworks and responsible AI-powered solutions.

The Data Engineer II supports data infrastructure operations, resolves issues, ensures platform stability, and implements improvements while collaborating with engineering and analytics teams.

Own and modernize Upsides analytics data platform: migrate pipelines, reduce cost, improve governance, design reusable modeling/orchestration patterns, deliver domain-critical data products, lead cross-functional initiatives, mentor engineers, and support ML and product teams.

Design, build, and optimize SQL and NoSQL database solutions (PostgreSQL, Elasticsearch, DynamoDB). Develop stored procedures, functions, triggers, and complex queries. Implement CDC and AWS DMS, manage platform via GitHub/CI-CD and Terraform, monitor and tune performance, participate in Level 3 on-call, and collaborate with analysts, architects, and developers to deliver scalable data services.

The Data Engineer II will build and maintain data pipelines and warehouses, ensure data quality, and collaborate with cross-functional teams to support business intelligence and machine learning efforts.

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.

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.

Designs, builds, and supports scalable data services and infrastructure across multiple platforms. Collaborates with users, developers, operations, and business partners to define requirements, perform data migration and transformation, ensure data accuracy/security, and maintain availability and scalability. On-call rotation and in-office presence required.

Deliver data engineering and ETL solutions to support client analytics. Build and maintain data pipelines, optimize SQL, design data architecture (star/snowflake models), create BI reports/dashboards, improve data quality, and support client engagements in regulated environments while learning and growing technical and client-facing skills.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

The Data Engineer will architect and develop robust data frameworks for data platforms, focusing on data transformations and automated workflows. Responsibilities include collaborating with stakeholders, ensuring data quality, and maintaining high engineering standards.

Develop and maintain data contracts, metadata standards, AI-readiness scoring pipelines, and governance/compliance tooling. Build quality monitoring, semantic layer assets, and AI-augmented development workflows. Collaborate with data owners and platform teams to onboard assets, ensure data trust, and enable AI-driven analytics.

Design, build, and maintain scalable data pipelines and infrastructure (AWS, MWAA Airflow, Snowflake). Troubleshoot, optimize performance, write technical documentation, and support data-driven business decisions using SQL, Python, and CI/CD practices.

Design and implement data pipelines, manage data with SQL and Python, automate workflows, ensure data integrity, and build scalable data solutions on AWS and Redshift.

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.