Data Platform Engineer

Develop and maintain backend systems.

Collaborate with front-end engineers on API design.

Architect enterprise cloud solutions using AWS.

Migrate workloads from on-prem to AWS.

Architect enterprise-grade cloud solutions.

Define cloud landing zones and reference architectures.

Design and build enhancements for the investment screening and fraud platform.

Manage the end-to-end software development lifecycle.

Design and maintain GitLab CI/CD pipelines.

Develop Docker environments for various stages.

Design and maintain MLOps pipelines.

Automate machine learning workflows.

Lead backend development using Node.js and TypeScript.

Design and maintain data-source integrations with a privacy focus.

Architect enterprise cloud solutions using AWS.

Migrate workloads from on-prem to AWS.

Architect enterprise-grade cloud solutions.

Define cloud landing zones and reference architectures.

Design and build enhancements for the investment screening and fraud platform.

Manage the end-to-end software development lifecycle.

Design and implement data products for analytics.

Transform raw data into actionable insights.

Design and maintain GitLab CI/CD pipelines.

Develop Docker environments for various stages.

Design and maintain MLOps pipelines.

Automate machine learning workflows.

Design and build data ecosystems.

Own end-to-end data pipelines.

Develop analytics capabilities across business units.

Improve development patterns and foundational datasets.

Define and execute data science strategy.

Lead AI/ML initiatives in healthcare/insurance.

Design and maintain marketing analytics data models.

Collaborate with cross-functional teams to define data needs.

Lead the centralized analytics function.

Develop and execute analytics vision and strategy.

Lead the company's analytics function.

Oversee data use cases and analytics platforms.

Define a research roadmap for strategic questions.

Lead quantitative research on user behavior.

Lead a team of product analysts.

Define and drive the analytical vision.

Lead and develop multiple data sub-teams.

Manage Data Lakehouse architecture and tooling decisions.

Design systems for product telemetry and analytics.

Ensure data reliability and scalability.

Lead the analytics engineering team to scale the data warehouse.

Mentor and provide feedback to junior engineers.

Design and maintain scalable data models.

Apply software engineering practices to analytics code.

Own end-to-end data architecture.

Build and manage batch data pipelines.

Lead and develop data teams.

Own and manage Data Lakehouse architecture.

Design and maintain data pipelines.

Optimize SQL queries and data models.

Provide data-driven insights for marketing strategy.

Measure impact of community and advocacy programs.

Lead and develop data teams.

Manage Data Lakehouse architecture.

Provide data-driven insights for marketing strategy.

Measure impact of community and advocacy programs.

Partner with teams for data-driven insights.

Develop scoring models for lead prioritization.

Collaborate with healthcare clients.

Translate data challenges into insights.

Provide data-driven insights for marketing strategy.

Measure community program impacts on adoption and pipeline.

Design and maintain data models.

Collaborate with teams for metric translation.

Build a self-service analytics system for the GTM team.

Integrate CRM, product usage, and marketing signals into a reporting model.

Provide data-driven insights for marketing strategies.

Measure impact of community and advocacy programs.

Lead and develop data engineering and analytics teams.

Manage the Data Lakehouse architecture and tooling decisions.

Transform questions into analytics recommendations.

Build models and conduct analyses for roadmap items.

Design and maintain scalable data pipelines.

Ensure data reliability, accuracy, and timeliness.

Design and maintain scalable data pipelines.

Write clean and well-documented code.

Provide data-driven insights for marketing strategy.

Measure impact of community and evangelism programs.

Build and lead the Data & ML team.

Own and manage the revenue data layer.

Lead and develop data teams.

Manage data architecture and tooling decisions.

Drive the strategy and design of scalable data platforms.

Provide technical leadership in architecture and design reviews.

Drive strategy and delivery of scalable data platforms.

Provide technical leadership through architecture and design reviews.

Provide data-driven insights for marketing strategy.

Measure impact of community and developer programs.

Provide data-driven insights to inform marketing strategy.

Establish proactive insights across the Marketing function.

Lead and develop data teams.

Manage Data Lakehouse architecture.

Lead the design and delivery of scalable data platforms.

Provide technical leadership and oversee project completion.