Top Senior Data & Analytics Jobs

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Lead and evolve first-party card fraud strategies by translating ambiguous problems into data-driven hypotheses and actions. Monitor performance and customer friction, leverage internal/external data to reduce false positives, define success metrics, partner with Product and Operations to implement scalable controls, execute changes with governance, and mentor team members while communicating executive-ready recommendations.

Design, build, and optimize scalable batch and streaming data pipelines and workflows. Drive data governance, modeling, cataloging, access control, and performance tuning. Mentor engineers, translate business needs into data-lake/warehouse solutions (AWS/Snowflake), use enterprise AI responsibly to accelerate SDLC tasks, and collaborate with analytics and business stakeholders to support regulatory and business objectives.

Lead design and development of the Treasury DataMart: enrich and transform transaction-level data, build and optimize data pipelines, ensure data quality, enable treasury analytics and reporting, collaborate with Technology and Finance, and present insights to senior leaders.

Design and automate reporting and workflows for financial operations. Analyze and troubleshoot data pipelines, build reports, collaborate with SMEs to remove manual bottlenecks, and own small optimization projects to improve operational efficiency.

Belvedere Trading seeks a Senior Data Engineer to enhance trading performance through data availability and reliability, designing ETL pipelines and data architectures in a fast-paced environment.

Build and own the data warehouse, analytics, and self-serve BI; define metrics and data models; prepare datasets for ML/AI features; partner cross-functionally to deliver dashboards, ensure data quality, and document systems as the companys first analytics hire.

Lead Data Engineer responsible for designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborate across Agile teams, work with distributed systems, big data and streaming tools, data warehousing, and mentor engineers. Ensure code quality via unit tests and performance tuning, and partner with product managers to deliver scalable data experiences.

Design, build, and maintain scalable ETL/ELT pipelines and data warehouse architecture. Partner with cross-functional teams to define KPIs, ensure data quality and governance, own billing data infrastructure, and deliver dashboards and analytics for leadership while mentoring peers.

Transform data into actionable insights by building dashboards, reports, and visualizations. Partner with cross-functional teams and data engineering to ensure data quality, lineage, and AI-ready metadata. Audit datasets, define monitoring rules, document data dictionaries, pilot AI-assisted BI tools, and apply hypothesis-driven analysis to surface insights that inform business decisions.

Design and drive enterprise-scale, hybrid cloud data architectures; define patterns and reference architectures; modernize legacy systems; ensure security, scalability, resiliency, and cost-effectiveness; mentor teams and advise senior leadership; enable data integration for analytics and AI workloads.

Design and lead enterprise-scale data architecture across hybrid and on-prem platforms. Define patterns for application, analytics, workflow, and AI workloads, mentor teams, influence senior leadership, and drive modernization, security, scalability, and TCO/ROI decisions.

Lead enterprise data management strategy to improve data quality, governance, metadata, and lineage. Advise senior leadership, design complex analyses and remediation, drive cross-functional solutions, and modernize practices using automation, analytics, and AI-enabled capabilities.

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.