• Design, develop, and maintain scalable data pipelines and ELT/ETL workflows on Snowflake, integrating data from diverse internal and external sources. • Architect and optimize Snowflake data models, schemas, and warehouses for performance, reliability, and cost efficiency. • Implement and enforce data governance, security, role-based access controls, and data quality standards across the platform. • Monitor and tune warehouse performance, query execution, and resource consumption to control costs and meet SLAs. • Build and maintain CI/CD pipelines for data infrastructure using tools such as dbt, Git, and orchestration frameworks (e.g., Airflow). • Leverage advanced Snowflake features — Snowpipe, Streams, Tasks, Time Travel, Dynamic Tables, and Snowpark — to deliver near-real-time and automated data solutions. • Collaborate with analysts, data scientists, and business stakeholders to translate requirements into robust data solutions. • Mentor junior engineers, conduct code reviews, and establish best practices for data engineering across the team.

• Architect, build, and maintain scalable ETL/ELT pipelines on the Databricks Lakehouse Platform using PySpark, Spark SQL, and Delta Lake. • Design and implement medallion (bronze/silver/gold) data architectures and enforce data quality, governance, and lineage standards. • Optimize Spark jobs and cluster configurations for performance and cost, including partitioning, caching, and autoscaling strategies. • Implement and manage Unity Catalog for access control, data governance, and cross-workspace asset sharing. • Build and orchestrate workflows using Databricks Workflows, Delta Live Tables, and CI/CD pipelines. • Collaborate with data scientists, analysts, and business stakeholders to translate requirements into reliable data products. • Establish engineering best practices, conduct code reviews, and mentor junior data engineers. • Monitor production pipelines, troubleshoot failures, and drive root-cause analysis and continuous improvement.

• Architect and manage BMS integration across colocation and Lambda-owned facilities, covering chillers, CRAHs, CDUs (Coolant Distribution Units), cooling towers, UPS systems, PDUs, and automatic transfer switches. • Define standards for BMS point lists, naming conventions, control sequences, and integration protocols (BACnet, Modbus, SNMP, OPC-UA, RESTful APIs). • Oversee commissioning and acceptance testing of new BMS deployments and CDU/TCS loop integrations for next-generation liquid-cooled GPU rack systems. • Collaborate with colocation partners (Equinix, Digital Realty, and others) to ensure telemetry data flows from provider BMS/EPMS into Lambda's monitoring stack. • Own the DCIM platform strategy and roadmap — evaluating, selecting, and implementing tooling for asset management, capacity planning, environmental monitoring, and power chain visibility. • Develop and maintain real-time dashboards for PUE, thermal performance, stranded capacity, and cooling system efficiency across all Lambda sites. • Build and maintain telemetry pipelines ingesting data from BMS, PDUs, in-rack sensors, CDUs, and network devices into centralized monitoring and alerting platforms (e.g., Prometheus, Grafana, InfluxDB, or equivalent). • Define alarm thresholds and escalation workflows for critical facility events including high coolant temperatures, CDU inlet/outlet anomalies, leak detection, and power exceedances. • Develop control strategies and setpoint frameworks for TCS (Thermal Control System) loops supporting direct liquid cooling at densities of 220–380 kW per rack. • Evaluate and qualify CDU vendors on controls integration capabilities, telemetry exposure, and remote management interfaces. • Define and enforce operational procedures for CDU commissioning, setpoint changes, loop pressure management, and fluid quality monitoring. • Support design and construction coordination for liquid cooling infrastructure in new data center buildouts, ensuring BMS and controls readiness at Day 1. • Establish and maintain facility event management processes, including on-call response protocols for facility telemetry anomalies. • Lead root cause analysis for facility system failures and implement corrective actions to prevent recurrence. • Partner with the data center operations team to maintain and refine emergency response runbooks tied to BMS alerts and automated controls. • Drive continuous improvement in MTTR for facility-related events through better telemetry coverage and automated remediation. • Manage BMS integrators, DCIM vendors, and control subcontractors - from RFP through design, installation, commissioning, and ongoing support. • Serve as the primary technical interface with colocation providers on all BMS/EPMS integration topics. • Collaborate with Lambda's infrastructure engineering, construction, and procurement teams to align controls requirements with facility buildout timelines. • Support due diligence and technical evaluation for new colocation sites and modular data center deployments from a telemetry and controls readiness perspective.

Role Description The Forward Deployed Engineer (FDE) is a senior, hands-on technical role embedded with Alteryx’s most strategic enterprise customers and partners. Your mission is to make Alteryx One a core part of modern data and AI architectures by turning existing analytics workflows into AI-ready, cloud-native solution patterns that scale through partners. This is not a traditional professional services or slideware architect role. As an FDE, you will design, build, and validate real production systems, then package what works into repeatable patterns that partners can sell and deliver independently. What You’ll Do - Design and build AI-ready data architectures using Alteryx One, anchored to cloud data platforms such as Snowflake, BigQuery, Databricks, and AWS - Convert existing analytics workflows into deployable solution patterns that can be reused across customers - Deliver lighthouse customer deployments that move from pilot to production in ~90 days - Partner closely with systems integrators and consulting partners to enable partner-led delivery - Work alongside Sales Engineers and Product teams to validate solutions in production and provide structured product feedback - Create documentation, reference architectures, and playbooks so solutions can scale without ongoing FDE involvement What You’ll Own - Technical leadership from architecture → pilot → production → partner handoff - Architecture decisions for cloud data platform and analytics modernization - Creation of repeatable patterns that drive faster time-to-value, expansion, and consumption - Clear exit criteria for every engagement—success is partner-led delivery, not long-term dependency What Success Looks Like - Success in this role is measured by what reaches production, what scales, and what continues without your direct involvement. - Ship production systems - Move enterprise customers from existing analytics workflows to AI-ready, cloud-native architectures built on Alteryx One - Take solutions from pilot to production in ~90 days - Create scalable solution patterns - Design architectures, workflows, and deployment models that are reused across customers - Establish repeatable patterns that become reference implementations - Enable partner-led delivery - Hand off solutions to systems integrators and consulting partners with clear ownership and exit criteria - Ensure partners can sell, implement, and extend solutions independently - Drive faster customer outcomes - Reduce time-to-production for analytics and AI initiatives - Deliver measurable impact, including cost reduction, faster insights, and improved operational efficiency - Shape the product with real-world evidence - Provide clear, structured feedback based on live deployments - Influence roadmap and product direction by validating solutions in production What You Bring - 7+ years building data, analytics, or platform systems in production - Hands-on experience with at least one major cloud data platform (Snowflake, BigQuery, Databricks, Redshift, or AWS) - Experience working directly with enterprise customers in a technical, field-facing role Technical Skills - Strong background in analytics workflows, data pipelines, or analytics engineering - Comfortable writing production workflows and lightweight code (primarily Python and SQL; APIs as needed) - Solid understanding of cloud architecture, governance, and cost tradeoffs Ways of Working - Builder mindset—you prefer shipping real systems over creating slides - Comfortable operating in ambiguity and giving candid technical guidance - Motivated by scale, reuse, and partner leverage rather than one-off wins Compensation - Base salary range for this role in the United States is $202,500-$225,000 with On-Target-Earnings range of $270,000-$300,000. - A monthly Connectivity Plus stipend of $150 to support remote work-related expenses - An annual $200 home office reimbursement Benefits - Medical, dental, and vision coverage - 401(k) with company match - Paid parental leave, caregiver leave, and flexible time off - Mental health support and wellness reimbursement - Career development and education assistance