Top Data & Analytics Jobs

Build and operate data platform services, APIs, SDKs, schemas, validation tools, event-driven pipelines, and reverse ETL workflows. Improve Kafka-based data delivery, schema evolution, observability, lineage, access controls, privacy, and compliance. Partner with engineering, analytics, data science, security, and infrastructure teams to create reliable, self-service data workflows. Operate AWS services, infrastructure as code, monitoring, alerting, and incident response.

Summer data analytics engineering internship focused on profiling new MLS data, extending dbt models, building warehouse transformations, adding tests and documentation, defining metrics, and supporting semantic models and dashboards. The intern will own a project, collaborate with analytics and data engineers, product managers, and customer-facing teams, and deliver production-ready solutions. This is a full-time, fully in-person role in New York City from June 7 through August 13, 2027.

Analyzes and documents ERP business and technical requirements, develops Workday financial and operational reports and dashboards, improves processes, validates data accuracy, supports system implementations and upgrades, and partners with finance, IT, product owners, and vendors on Workday solutions and lifecycle management.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Analyzes game monetization data to improve key performance indicators, evaluate A/B tests, identify opportunities, and recommend solutions. The role queries databases using HQL and SQL, analyzes results with Excel and statistical tools, prepares weekly management presentations, provides status reporting, and performs ad hoc analysis.

Develops Power BI dashboards, leads data migration to a new fundraising and ERP system, validates and reconciles data, gathers reporting requirements, builds Power Automate workflows, documents processes, supports staff, and coordinates third-party data integrations. The role requires strong analytics, business intelligence, SQL, data modeling, reporting, and stakeholder communication skills, with approximately five years of experience.

Build and maintain scalable dbt data models, transforming raw data into trusted, documented datasets. Own warehouse data modeling and metric definitions, establish testing and code review standards, monitor data quality, optimize query performance, and troubleshoot reporting discrepancies. Partner with engineers, analysts, and business stakeholders to translate business needs into reliable pipelines. Mentor junior engineers and contribute to technical best practices and tooling decisions.

Lead and develop an analytics engineering team, define the analytics roadmap, and establish scalable standards for data modeling, business intelligence, and AI-enabled analytics. Partner with leadership, Operations, Engineering, and Product to solve complex business problems, deliver actionable insights, optimize data pipelines, and build a trusted data foundation. Champion documentation, data literacy, and evidence-based decision-making across the organization.

Lead and grow Confido’s data engineering team, initially managing an analytics engineer while remaining hands-on with SQL, data models, pipelines, and code reviews. Own warehouse architecture, orchestration, tooling, data standards, monitoring, and metric definitions. Hire and develop engineers, prioritize the team’s roadmap, collaborate across departments, and improve data reliability, documentation, and reporting across the company.

Designs, builds, and maintains complex data systems, including scalable data infrastructure, batch and streaming pipelines, data models, data products, and cloud-based architectures. Develops automated deployment pipelines, improves data systems, and supports data analysis and advanced analytics. Collaborates with cross-functional teams to gather requirements and ensure robust, governed, and accessible data solutions.