Top Senior Data & Analytics Jobs

Build the financial reporting function for a new iCasino division from scratch. Own data modeling, SQL and dbt transformations, dashboards, recurring reports, KPI tracking, and business insights using Snowflake, Sigma, and Oracle. Partner with engineering, finance, compliance, operations, and commercial teams; communicate findings to stakeholders; develop scalable BI solutions; leverage GenAI tools; and mentor others on analytics and BI best practices.

Conduct media mix modeling, experimentation, incrementality analysis, forecasting, and marketing performance reporting. Build scalable analytical tools, automated workflows, Tableau dashboards, and statistical resources using SQL, Python, and BI platforms. Partner with Marketing, Data Science, Data Engineering, Finance, and Sales teams to improve measurement capabilities, operationalize data structures, and translate complex findings into actionable business recommendations. Maintain analytical standards, documentation, and quality controls.

Analyze frontline operations performance across specialists, queues, vendors, and sites. Identify root causes behind SLA, AHT, accuracy, and utilization changes; quantify impacts; recommend corrective actions; define metric and dashboard requirements; and communicate concise, actionable findings to operations leaders. The role uses SQL and AI-enabled analytics tools to accelerate analysis and supports performance monitoring in a multi-vendor, multi-site contact-center environment.

Leads consumer insights and research initiatives, translating customer experience data, market dynamics, cultural trends, and analytics into recommendations for brand, marketing, product, and business decisions. Develops customer journey maps, conducts qualitative and quantitative research, applies AI-enabled analysis, influences cross-functional teams, builds closed-loop feedback processes, presents decision-ready narratives, and manages research and vendor partnerships.

Analyze exchange datasets across equities, FX, options, and futures; develop liquidity and order-book metrics; and create commercial, client-ready data products. Apply statistical and machine learning methods to identify patterns, anomalies, and predictive signals. Partner with engineering, sales, marketing, internal teams, and clients on data ingestion, normalization, cloud distribution, product specifications, competitive analysis, and market-data commercialization.

Lead production data engineering workstreams for AI-enabled financial services solutions. Design Python and SQL pipelines, warehouse and lakehouse models, document ingestion and retrieval-ready data foundations. Implement data quality, lineage, access controls, testing, deployment, monitoring, and recovery processes. Mentor engineers, manage technical delivery, collaborate with clients and vendors, and contribute to reusable assets, proposals, and implementation roadmaps.

The Data Governance Analyst investigates data quality failures, writes SQL validation queries, extracts and analyzes data, and develops governance metrics. The role partners with business and technology teams to implement data policies, risk controls, taxonomies, and governance frameworks. Responsibilities include tracking data issues, coordinating enterprise strategy rollouts, synthesizing performance metrics, and recommending improved policies and processes. The position requires strong SQL, Python or SAS proficiency, stakeholder communication, and data governance experience.

Build and maintain metadata-driven ingestion frameworks, batch and streaming ELT pipelines, lakehouse data models, and governed Gold data products on Databricks. The role includes data quality, governance, lineage, catalog management, AI-ready datasets, APIs, reverse ETL, production monitoring, and documentation. Candidates will use Python, PySpark, SQL, Git, and CI/CD while collaborating with analytics, architecture, data science, stewardship, and global managed-services teams.

Leads ad hoc and deep-dive analyses, predictive modeling, forecasting, variance analysis, and root cause analysis for Cloud Success leadership. Partners with cross-functional stakeholders, translates complex findings into executive recommendations, and owns analytical projects from problem framing through presentation. The role also requires advanced SQL, visualization expertise, strategic business partnering, stakeholder management, and mentoring junior analysts.

Build and operate Imagine Worldwide’s AWS- and ClickHouse-based analytics platform, including infrastructure, data lakes, Spark processing, ingestion pipelines, Airflow orchestration, dbt models, CI/CD, monitoring, security, backups, and cost optimization. Process billions of device events and integrate program context into reliable analytical datasets. The role also involves incident response, documentation, architectural contributions, and collaboration with globally distributed analytics and operations teams.