Top Data & Analytics Jobs

Analyze marketplace and product performance using SQL, support experiments and advertising-effectiveness studies, build reusable datasets, QA metrics, and document analytics. Collaborate with analytics engineers, data scientists, product managers, engineers, and commercial teams while developing skills in statistics, causal inference, Python/R, and marketing science.

Build and lead Apptronik’s People Analytics function, developing workforce analytics strategy, predictive models, dashboards, KPI frameworks, and data governance. Advise executive leadership on talent strategy, organizational health, workforce planning, and organizational design. Establish people-data standards, security, access controls, compliance practices, and architecture in partnership with IT and Data Engineering. Translate workforce data into actionable business narratives while mentoring analytics teams and supporting HR initiatives.

Leads the strategy, architecture, and deployment of AI-enabled business intelligence infrastructure. Designs governed semantic layers, data models, and BI systems using dbt and enterprise data warehouses. Manages and coaches a technical engineering team, partners with executives and cross-functional stakeholders, and ensures reliable, high-performing data pipelines, warehouses, and reporting tools. The role also advances AI-driven analytics, data discovery, and self-service reporting across the organization.

Supports enterprise data management and reporting for an investment management SaaS platform. Responsibilities include maintaining investment datasets, preparing reports and dashboards, performing data quality checks, documenting data governance standards, supporting ad hoc analytics, and archiving reports for compliance. The intern collaborates across teams and gains experience in financial data, investment analytics, and enterprise data governance.

Analyze player behavior, product performance, revenue, retention, and engagement for Zynga Poker. Design experiments, dashboards, ETL pipelines, telemetry, fraud models, and business intelligence solutions. Partner with product, game design, economy, and consumer insights teams to guide decisions and improve player experiences. Contribute to AI-enabled analytics workflows, game integrity initiatives, and analytics best practices.

Own customer data migrations from legacy systems through QA and launch. Clean, map, import, and validate event, customer, order, donation, ticketing, and marketing data. Configure and troubleshoot integrations, APIs, webhooks, sync failures, and production data issues using SQL, logs, dashboards, and administrative tools. Create migration playbooks, documentation, validation processes, and rollback plans while partnering with customers, support, product, and engineering.

Build and scale data products supporting analytics, engineering services, and AI/ML models. Responsibilities include optimizing queries and data pipelines, developing data models and governance processes, administering ETL/ELT and reverse ETL orchestration, and creating documentation and training. The role also involves mentoring, interviewing, and hiring data engineers while helping define the organization’s data engineering strategy.

Build and maintain reliable ELT/ETL pipelines using Snowflake, Dataiku, Databricks, SQL, and Python. Create finance data marts, automate workflows, enable Tableau analytics, and implement data quality, governance, monitoring, testing, and CI/CD practices. Partner with Finance, FP&A, Accounting, and business analysts to translate financial requirements into scalable, documented data products and modernize manual processes.

Lead end-to-end quantitative market research, including research planning, surveys, advanced statistical analysis, synthesis, and stakeholder presentations. Build and manage brand and sentiment measurement programs, identify customer and market trends, and provide strategic recommendations for growth and product opportunities. Partner with business leaders, promote research-driven decision-making, and mentor junior researchers while scaling the organization’s research practice.

Build data pipelines and relational data models using Python and SQL; clean operational data, support scenario analysis, and create dashboards for PMO leadership. Explore LLMs and machine learning for text analysis and data-quality automation. Gain production experience with testing, documentation, maintainable code, and data-driven decision support in a trading environment.