Top Senior Data & Analytics Jobs

Leads data engineering architecture and modeling initiatives, designing scalable pipelines, cloud data platforms, and reusable data solutions. Collaborates with engineering, product, analytics, and data science teams while mentoring developers and establishing common design patterns. Responsibilities include distributed data processing, relational and NoSQL systems, data security, observability, streaming pipelines, and operational excellence across platforms such as Databricks and Snowflake.

Leads data analysis strategy, mining large datasets, building and validating models, improving reporting and processes, evaluating products and initiatives, and consulting on data systems. Manages and mentors junior analysts, drives business intelligence efforts, collaborates across business units, and oversees concurrent analytical projects. Requires strong programming, quantitative analysis, communication, leadership, and problem-solving skills.

Leads data engineering teams and technical strategy for scalable cloud data platforms. Oversees architecture, pipelines, data models, quality, observability, and operational standards across Databricks, Spark, Python, and Snowflake environments. Mentors engineers, drives large-scale data initiatives, evaluates platform choices, and collaborates with product and software teams to deliver reliable data solutions.

Leads teams responsible for enterprise data engineering, analytics platforms, data modernization, governance, and AI context-layer development. Oversees data pipelines, models, integrations, metadata, ontologies, knowledge graphs, reliability, security, compliance, and cloud adoption. Sets strategy, manages budgets and resources, partners with executives and product leaders, drives risk controls and operational excellence, and develops engineering talent. The role also supports AI assistants and self-service analytics through governed enterprise data, semantic layers, and contextual metadata.

Leads development, validation, implementation, and monitoring of credit scorecards, loss forecasting, collection, recovery, and pricing models for auto lending. Provides strategic recommendations balancing growth, profitability, risk, and regulatory compliance. Partners with Pricing, Risk, Finance, and senior leadership, translating complex statistical analyses into actionable business insights. Uses large-scale data and advanced analytics to monitor portfolio performance, identify risks, and improve forecasting accuracy.

Performs moderately complex analytics, reporting, and data research supporting Financial Crimes Operations. Analyzes business, operational, and technical challenges; extracts insights from complex data environments; develops metrics and visualizations; supports strategic initiatives; and identifies engineering enhancements. Leads cross-functional projects, mentors staff, manages work independently, and applies compliance and risk-management requirements.

Own measurement strategy and performance improvement for Snap’s advertising measurement solutions. Lead experimental design, causal analytics, benchmarking, meta-analyses, governance, and scalable measurement initiatives. Advise executives and external partners, influence product roadmaps, develop thought leadership, and build analytical fluency across teams. Apply advanced statistics, SQL, AI tools, dashboarding, and data visualization to guide advertising efficacy decisions and identify innovation opportunities.

Leads end-to-end data enablement for Supply Chain and Manufacturing domains. Partners with business and technical stakeholders to define use cases, roadmaps, data models, semantic layers, integration requirements, quality controls, and governed data products. Builds and optimizes Databricks lakehouse pipelines using SQL, Python, PySpark, and Delta Lake across complex ERP, manufacturing, procurement, inventory, and finance systems. Establishes standards for lineage, security, documentation, reliability, and production readiness.

Architects and implements governed, cloud-native analytics engineering solutions on Databricks, including Lakehouse pipelines, semantic models, data governance, and self-service BI. Builds Python/PySpark ingestion pipelines, develops Databricks SQL models, and delivers Tableau or Sigma dashboards. Partners with data owners, business users, and senior stakeholders to support adoption, onboarding, enablement, and executive decision-making in a regulated risk and compliance environment.

Build and operate scalable Databricks-on-AWS data pipelines using PySpark, Delta Lake, and lakehouse patterns. Optimize performance, implement data quality, monitoring, alerting, and automated remediation, and deliver curated datasets for BI and analytics partners. Collaborate with stakeholders on architecture and design while applying secure software engineering, CI/CD, agile, and operational stability practices. The role also uses AI-assisted development tools and supports workforce data analytics.

Own analytics engineering for the machine infrastructure supply chain team. Build and maintain semantic layers, trusted data models, metrics, dashboards, and analytics-ready datasets using dbt and modern data platforms. Analyze inventory trends, optimize stock levels, investigate discrepancies, and develop supply chain KPIs. Partner with data engineering, finance, product, operations, planners, and buyers while improving inventory planning, replenishment, ERP processes, and warehouse efficiency.

Owns complex consumer experience reporting and analytics initiatives, developing scalable data models, dashboards, scorecards, and executive insights. Partners with product, operations, engineering, and data teams to define metrics, ensure data quality, govern reporting standards, and improve self-service analytics. Leads functional projects, resolves ambiguous analytical challenges, mentors analysts, and evaluates automation and AI-enabled capabilities.

Designs and maintains SQL/Python data pipelines, warehouse models, and Power BI dashboards supporting finance planning, forecasting, budgeting, and book close. Owns solution architecture from requirements through deployment, validates data and systems, resolves process and reliability gaps, and manages multiple enhancements. Collaborates with business and technical stakeholders, mentors analysts, leads continuous improvement, documents solutions, trains users, and develops automated and AI-enabled efficiencies.

Develop and deploy Python data pipelines that create business data objects for applications. Design and implement source-to-target mappings, CI/CD pipelines, and software solutions; resolve complex technical issues; and perform unit and integration testing. The role requires maintaining high software quality, applying software development lifecycle and architecture expertise, and supporting continuous team improvement. Candidates should have experience with Git, AWS services, Snowflake, generative AI developer tools, and AI coding assistants.

Lead and grow a global data engineering team while establishing foundational data platforms and engineering standards. Design and oversee GCP-based pipelines, medallion data models, DataOps, monitoring, SLAs, CI/CD, governance, and data quality practices. Drive AI adoption across engineering workflows and build reliable, AI-ready data. Partner with business and technology stakeholders to prioritize roadmaps, deliver scalable solutions, and support data democratization.