Top Hybrid Data & Analytics Jobs

Lead the architecture and evolution of enterprise-scale data platforms, applying distributed-systems and advanced data-modeling patterns. Establish technical standards across dbt, Airflow, Snowflake, Kafka, ETL, cloud infrastructure, security, monitoring, CI/CD, testing, and source control. Solve complex data problems, partner with senior leaders, provide technical governance, mentor engineers, and drive platform and process improvements.

The Data Governance Analyst implements and manages enterprise data governance initiatives, including data quality, metadata, data cataloging, master data management, and profiling. The role partners with data owners, stewards, and technical teams to establish standards, maintain business definitions and lineage, monitor quality metrics, resolve data issues, support MDM processes, improve data capture, and promote governance adoption and data literacy.

Designs and maintains scalable data pipelines, ETL/ELT workflows, data models, data lakes, warehouses, and curated data layers. Integrates structured and unstructured data from internal systems, vendors, and APIs, while supporting Tableau dashboards and analytics. Monitors pipeline reliability, performance, governance, validation, security, and data quality. Collaborates with analysts, data scientists, developers, and stakeholders to deliver cloud-based data solutions, documentation, and AI-enabled analytics capabilities.

Lead the design and evolution of an enterprise AWS data platform supporting analytics, reporting, operational decisions, and future AI initiatives. Build reliable batch and near-real-time pipelines, establish data architecture and governance standards, implement infrastructure as code and CI/CD, and develop governed data products. Provide hands-on technical leadership, architectural direction, mentoring, and cross-functional collaboration while balancing scalability, performance, reliability, security, and cost.

Conduct market, competitive, customer, product, industry, and performance analysis to support data-driven decisions across Lifecycle Services. Develop market insights and plans, guide go-to-market activities, monitor competitors, build cross-functional data-sharing programs, and support voice-of-customer, partner, and employee research. Collaborate with product management, marketing, sales, and global teams while using AI-based tools and quantitative and qualitative research methods.

Develops pre-trade analytics, risk monitoring, P&L reporting, pricing and hedging models for mortgage-backed securities and rates products. Partners with traders and sales to analyze MBS pools, TBA spreads, and interest-rate strategies. Automates data workflows, improves trading infrastructure, conducts quantitative research and backtesting, and uses market analytics platforms to support front-office trading decisions.

Research global logistics trends, competitor activity, industry developments, and macroeconomic factors. Analyze qualitative and quantitative data to identify opportunities and risks, then translate findings into strategic recommendations and transformation initiatives. Create reports, presentations, dashboards, and business cases for senior leadership while tracking the impact of implemented strategies.

Designs and implements enterprise database architecture, relational databases, data warehouses, data models, archiving strategies, and migration solutions. Develops database security, backup, and recovery specifications; provides MIS and database operational support; codes utilities; resolves user issues; and supports legacy-system migrations. The role also requires collaboration, technical training, and expertise in Oracle, ETL, PL/SQL, and dimensional data modeling.

Lead and manage end-to-end analytics and visualization projects: design visual storytelling, build and maintain analytical models, automate reusable self-service tools and reports, mentor staff, establish best practices, and coordinate data acquisition, deployment, and retirement.

Lead development of customer-facing analytics and internal dashboards by building advanced SQL models, optimizing visualizations, and maintaining data models. Drive analytics priorities, improve team processes, present findings to leadership, and participate in agile planning and peer review to support product, operations, and commercial teams.