Top Data & Analytics Jobs

The Staff Data Engineer will lead design and implementation of analytics platforms and data products, collaborate cross-functionally, mentor engineers, and drive best practices at Upside.

Run and analyze product experiments, define product metrics and telemetry for web and mobile, build funnel/retention/cohort analyses using SQL and Python on Snowflake, maintain Omni dashboards, create AI-agent workflows to automate routine analysis, and translate findings into clear product recommendations alongside PMs, designers, and engineers.

Lead and scale the data engineering organization to build data products and architecture that power GTM (marketing and sales) strategy. Define technical strategy, enable reporting, analytics, data science, ML/AI workloads, and partner with IT, product, analytics, and GTM stakeholders to deliver production-grade data platforms and services.

The Senior Analytics Engineer will manage the analytics layer, owning dbt projects, orchestrating data pipelines, defining metrics, and ensuring data quality while mentoring the data team.

Design, build, and deliver scalable data pipelines and products end-to-end. Partner with analytics, product, and engineering to ingest diverse data sources, implement dbt transformations, ensure data quality, CI/CD, observability, monitoring, and cost/performance optimization. Leverage AI/automation for self-service tooling and intelligent pipelines while driving stakeholder alignment and reliable production operations.

Implement and embed data and AI governance across Databricks and Snowflake platforms. Partner with delivery pods to automate quality gates, lineage, monitoring, and risk triage. Translate policy into technical controls, build observability for data drift and distribution, and optimize governance processes to accelerate compliant deployments.

Own and build canonical data models and metric definitions using dbt and cloud warehouses. Create tests, documentation, lineage, and data quality checks; reconcile metrics across teams and payer customers; partner with analysts, data scientists, product, engineering, and finance; optimize warehouse performance, cost, and PHI-aware access patterns.

Lead research strategy for Beauty, Style, and Entertainment verticals by analyzing large first-party datasets, synthesizing insights, advising marketing/editorial/sales, supporting RFPs and ad hoc requests, designing quantitative consumer research, and presenting findings to technical and non-technical stakeholders.

The Lead Data Analyst (Network) drives data-driven initiatives, dissects datasets, and provides actionable insights to enhance business performance and network efficiency.

Lead and grow a Business Intelligence team, partner with senior leaders to prioritize analytical initiatives, ensure analytic output quality, drive BI operating model evolution, adopt modern BI tooling and self-service, and embed AI-assisted methods to improve decision-making.