Top Hybrid Data & Analytics Jobs

Analyze post-sales customer lifecycle data, including adoption, retention, renewals, churn, and expansion. Build Salesforce reports and dashboards, query and validate data with SQL, and create recurring analyses and visualizations using Excel, Tableau, and other BI tools. Partner with Customer Success, CS Operations, Analytics, BI, and Sales Systems teams to improve reporting, investigate trends, identify risks, and communicate actionable insights to technical and non-technical stakeholders.

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.

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.

Owns fraud and market-abuse detection models, rules, and partner-facing surveillance reporting across trading data. Investigates wash trading, collusion, hedging, reverse trading, and other suspicious activity; calibrates thresholds, validates false positives, maintains data quality, and produces evidence-based findings. Requires strong SQL, cloud data warehouse experience, futures and derivatives knowledge, anomaly detection expertise, and the ability to communicate findings to partners and executives.

Analyze Experian portfolio repayment performance, credit risk, underwriting and credit-limit changes, collections, funnel metrics, cohort LTV, and unit economics. Own partner analytics models, data definitions, reporting, alerting, and data quality monitoring. Collaborate with Product, Engineering, Partnerships, Finance, and Experian to guide roadmaps, evaluate experiments, identify risks, and support business reviews.

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.

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.

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.