Senior Data Engineers on the Proprietary Research team design and build solutions that enable investment professionals to effortlessly extract insights from large, complex structured and unstructured data sets.
Compensation
The annual base salary range for this role is $250,000-$300,000, which does not include discretionary bonus compensation or our comprehensive benefits package.
Responsibilities
Develop and support big data processing pipelines including but not limited to ingestion, transformation, and end-customer delivery
Build out cloud-based infrastructure using distributed techniques for other data engineers / data scientists / researchers
Be at the forefront of new technology developments with respect to the handling and processing of big data and conduct proof of concepts evaluations of new technologies
Build and support visualization and exploration capabilities around our big data sets
Lead and deliver customer-facing technical projects on the Databricks platform, designing and building end-to-end data, AI, and application solutions.
Compensation
Zone 1 Pay Range
$182,000—$250,208 USD
Zone 2 Pay Range
$182,000—$250,208 USD
Zone 3 Pay Range
$182,000—$250,208 USD
Zone 4 Pay Range
$182,000—$250,208 USD
Responsibilities
Lead architecture and design decisions
Build and productionize solutions for customers
Integrate with client systems and train users
Deliver production-grade systems and reference architectures
Guide customers on strategic big data and AI projects
Key contributor on the Analytics Delivery Engineering team, building a clean, scalable, reliable, and automated services framework for Workday Federal deployments.
Compensation
Primary Location Base Pay Range: $151,500 USD - $227,300 USD
Additional US Location(s) Base Pay Range: $137,100 USD - $243,600 USD
Responsibilities
Design and implement solutions for Kubernetes platform, including infrastructure automation, CI/CD pipelines, and observability tools
Build and maintain core platform components, ensuring high availability, scalability, and security
Automate infrastructure provisioning, configuration management, and application deployments using Terraform and Argo CD
Provide support and troubleshooting for platform-related issues
Implement and maintain security best practices and compliance
Requirements
Bachelor's degree in a computer related field or equivalent work experience
Nice to have: Monitoring and observability tools
Nice to have: Security auditing and compliance frameworks
The Data Platform Engineering Team acts as the backbone of our enterprise data architecture, bridging the gap between Data Engineering, Infrastructure, and Operations. Responsible for architecting, scaling, and maintaining multi-cloud infrastructure across AWS and Azure, the team takes direct ownership of core Apache Airflow orchestration, big data frameworks, and containerized environments to ensure a robust, production-grade platform.
Compensation
Pay Range: $140,600.00 - $173,100.00
Responsibilities
Design, deploy, scale, and maintain highly available Apache Airflow clusters (using Helm, Kubernetes, and Terraform) to support critical enterprise ETL/ELT workflows.
Drive DevOps practices using Terraform, Helm Charts, and ArgoCD to automate platform deployments, manage self-hosted GitHub runners, and enforce GitOps workflows.
Architect and manage robust CI/CD pipelines utilizing GitHub Actions for seamless deployment of data pipelines, infrastructure components, and DAGs.
Provision and manage scalable Kubernetes (EKS/AKS) clusters, Docker containers, and underlying cloud infrastructure across AWS (EC2, EMR, S3, VPC) and Azure (Synapse, ADLS).
Build and maintain end-to-end monitoring, logging, and alerting systems using Grafana, Prometheus, Loki, and centralized log management solutions to ensure high platform uptime and reliability.
Lead the design and scaling of a unified data platform for Redfin’s enterprise data organization, serving as chief architect and technical north star.
Compensation
The compensation for this position is $234,000.00-$286,000.00. The position may also be eligible for an annual bonus, incentives, and other employment-related benefits including, but not limited to, medical, dental, and vision benefits, 401K retirement plan, and paid-time off.
Responsibilities
Design and implement a unified, open-format Data Lakehouse utilizing a Snowflake-managed Apache Iceberg architecture on AWS S3
Architect and scale resilient, high-throughput ingestion frameworks for batch, micro-batch, and real-time streaming patterns
Design a sovereign, domain-oriented data mesh within a single Snowflake environment with RBAC and Row-Access Policies
Spearhead deployment of a Universal Semantic Layer on top of Gold-tier data
Define technical standards for Medallion data flow and standardize ELT patterns using dbt, PySpark, and Snowpark
Senior Core Infrastructure Engineer responsible for building and supporting scalable cloud infrastructure, ensuring operational stability, performance, and cost-efficiency.
Compensation
The base pay range for this position is between $105,600.00 and $150,400.00 per year.
Responsibilities
Build and support scalable cloud infrastructure leveraging Azure and AWS
Troubleshoot cloud PAAS resources, networking and infrastructure
Align engineering activities with company goals
Implement DevOps practices and tools
Set up observability solutions
Requirements
Bachelor’s degree in Computer Science, Engineering, or a related technical field
Partner closely with Sales and AI Solution Practice Leaders to shape technical strategy, lead discovery engagements, design and deliver demos, and author proposals for pre-sales engagements.
Locations (3)
New York City, New York, United States
San Francisco, California, United States
United States
Compensation
Compensation range is $190K - $350K OTE. All offers also include equity in the form of employee stock options.
Responsibilities
Partner closely with Sales and AI Solution Practice Leaders to shape technical strategy across active prospects
Lead structured technical discovery engagements with prospects to understand business objectives, success criteria, data landscape, architectural constraints, security considerations, and organizational readiness
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
Build reusable data-to-AI solutions, architectures, and accelerators with partners on Google Cloud
Design and implement governed semantic layers and enterprise ontologies connecting BigQuery Graph, Conversational Analytics Agents, Looker, and Google Cloud Knowledge Catalog Custom Aspects to ground enterprise AI agents in verified business definitions
Validate Gemini Enterprise plugins, remote Model Context Protocol (MCP) servers, and reusable agent skills that integrate Google Data Cloud services with third-party enterprise systems
Develop internal AI-assisted validation tools, evaluation rubrics, and automated architectural review workflows that scale Partner Engineering quality and turnaround velocity across partner engagements
Lead technical onboarding for Data and AI launches, developing implementation guides, code assets, and enablement materials
Requirements
Bachelor's degree in Computer Science, Data Science, Engineering, or a related technical field
Translate discovery findings into well-defined GenAI solution architectures that demonstrate technical feasibility and business impact
Design, build and deliver bespoke demos, including evaluation pipelines, dataset strategy, retrieval-augmented generation systems, fine-tuning workflows, prompt engineering strategies, and agentic architectures tailored to specific customer use cases
Author and contribute to custom proposals, including Statements of Work and RFP/RFI responses, ensuring scope clarity, architectural soundness, realistic effort estimates, and alignment with delivery capabilities
Requirements
B.S. in Computer Science, Engineering, Math/Statistics, or equivalent experience
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