Top Data & Analytics Jobs

Leads enterprise analytical tool validation, assessing methodology quality, risk, technical issues, and business impact. Plans validation projects, researches emerging practices, develops solutions, communicates findings through reports and presentations, and mentors junior staff. The role requires extensive data analysis, statistical or econometric modeling, open-source data technology experience, data governance knowledge, predictive analytics expertise, and people management.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Lead analytical strategy for Talent Development and Organizational Insights, translating talent and organizational data into actionable recommendations for product teams and executives. Responsibilities include framing business questions, developing metrics and models, influencing product roadmaps, creating reusable data products, managing concurrent analytical delivery, and partnering with product, engineering, design, People Science, HR, and senior leaders.

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Leads data analysis and data product initiatives involving large databases, modeling, testing, reporting, process improvements, and product analytics. Manages and mentors junior analysts, partners with technology and business teams, supports data-system design, and develops strategic tests for products and marketing materials. Requires strong analytical, programming, business intelligence, communication, and leadership skills.

Design, build, test, and support scalable cloud data solutions and pipelines. Develop data platforms using Python, Spark, SQL, Databricks, Snowflake, relational and NoSQL databases, and cloud technologies. Establish reusable data engineering patterns, security controls, and operational standards. Collaborate with product managers, engineers, analysts, and data scientists; communicate technical outcomes; mentor data professionals; and deliver resilient, high-performance financial data products.

Lead data analytics strategy, mine and analyze large-scale datasets, build and validate models, improve reporting and operational processes, evaluate products and marketing initiatives, and consult on data systems. Manage and mentor junior analysts, collaborate across business units, oversee concurrent projects, and apply business intelligence tools to support customer experience and organizational decision-making.

Design, build, and operate scalable cloud data pipelines, platforms, and applications using distributed data technologies. Lead architectural decisions, evaluate platforms such as Snowflake and Databricks, establish engineering patterns, and deliver resilient data solutions. Collaborate with product managers, software engineers, analysts, and data scientists while mentoring engineers and communicating technical outcomes to stakeholders. Drive large-scale data initiatives independently in an Agile environment.

Design, build, and support scalable cloud data platforms, pipelines, and applications. Collaborate with engineering, product, analytics, and data science teams; develop distributed and real-time data workloads; enforce reusable engineering patterns; implement security and access controls; optimize resilience and operational efficiency; communicate technical outcomes; and mentor data professionals.

Design, develop, test, implement, and support cloud-based data engineering solutions. Lead developers working with machine learning, distributed microservices, and full-stack systems. Use Java, Scala, Python, databases, data warehouses, streaming technologies, and public clouds. Collaborate with product managers and Agile teams, conduct code reviews and unit testing, optimize performance, mentor engineers, and stay current with emerging technologies.