Top Tech Jobs & Startup Jobs

As a Tech Lead, you will own the technical direction of a B2B analytics platform, lead engineering teams, and ensure production reliability while collaborating with various stakeholders to translate business needs into technical solutions.

Own the conversion of raw alternative datasets into scalable, AI-ready data products. Define methodologies, metric definitions, and guardrails for AI-driven insights. Partner with engineering, product, and customers to expand use cases, ensure data quality, validate changes, and manage incidents to deliver reliable, explainable data products at scale.

The Senior QA Engineer will develop test plans, handle automated and performance testing, manage defects, and collaborate with teams for project progress.

The Client Strategy Manager will manage client relationships, develop commercial strategies, and ensure client success through proactive engagement and support. This role includes account management, upselling, and enhancing client engagement with the firm's analytics services.

The Data Operations Analyst develops custom product attribution based on client needs, utilizing tagging and machine learning; ensuring data mapping accuracy and project delivery.

As a Data QA Associate, you'll manage data cleaning and quality assurance processes, transforming receipt data into business insights, collaborating with remote teams, and ensuring data consistency and quality.

Lead an end-to-end data domain: define processing and validation, build reusable methodologies and QA/monitoring systems, partner with product and engineering to set technical direction, onboard new datasets, apply AI/automation to reduce manual work, and mentor junior analysts to scale domain capabilities.

Build and maintain end-to-end data pipelines using PySpark and SQL on Databricks/Delta. Establish data modeling and pipeline best practices, create AI-ready analytical datasets, troubleshoot pipeline issues, and collaborate with stakeholders to embed business logic and optimize reliability, efficiency, and performance.

Assess and evaluate third-party datasets, define success metrics and analytical frameworks, run quantitative analyses, and recommend product opportunities. Support new product development, collaborate cross-functionally with engineering and partnerships, leverage AI/LLMs and automation, and contribute to tooling, knowledge, and operational efficiency.

Design, validate, and deploy scalable statistical and distributional models for enterprise consumer analytics. Implement rigorous methodologies, convert ad-hoc analyses into version-controlled analytical libraries, and partner with Data Engineering to optimize performance and scale compute workloads (including cloud/Databricks). Deliver reproducible analytics powering dashboards and LLM-driven applications, with a focus on panel and transaction-level consumer data.