Sr. Data Engineer (Snowflake)
Design and implement scalable, cost-effective data solutions and pipelines within Snowflake for clients. Lead technical delivery, perform performance tuning, and mentor junior engineers in a consultative capacity.
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Design and implement scalable, cost-effective data solutions and pipelines within Snowflake for clients. Lead technical delivery, perform performance tuning, and mentor junior engineers in a consultative capacity.
Design and operate scalable batch and streaming data pipelines to power analytics and operational workflows. Develop curated data models and implement quality frameworks to ensure reliable, well-governed data access for internal stakeholders.
Design and build scalable data pipelines, integrations, and architectures to support a modern data ecosystem. Collaborate with data scientists to operationalize machine learning models and implement MLOps standards for production-grade AI initiatives.
Develop and maintain end-to-end data pipelines, Spark-driven workflows, and data APIs to support advanced analytics and automation. Build MCP servers and AI agents to integrate agentic workflows into the data engineering lifecycle.
Drive the architectural direction and technical decisions for BILL's core data platform, focusing on scalable infrastructure for ingestion, storage, and serving. Lead the design of complex data capabilities and mentor senior engineering staff to maintain high technical standards.
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Build and own the infrastructure for reliable, well-governed data powering products and business metrics. Lead the evolution of the data platform toward an AI-first architecture and manage the full data stack from ingestion to delivery.
Lead the technical architecture and extension of the configuration-driven data platform to support product insights and ML/LLM infrastructure. Mentor a team of engineers while managing the full DevOps lifecycle of ingestion, curation, and retention services.
Design, build, and maintain scalable data pipelines and analytical data models using a modern data stack. Ensure data accuracy and reliability for reporting and business decisions through proactive monitoring and Level 1 support.
Build and scale a data lakehouse on AWS to support financial analytics, reporting, and AI-driven product features. Develop robust ETL/ELT pipelines and integrate vector databases to enable semantic search and machine learning capabilities.
Define and drive the technical strategy for high-priority backend and data platform initiatives to support global AI and machine learning demands. Architect scalable distributed systems and mentor senior engineers to foster a high-standard engineering culture.
Lead the platform-specific build to migrate health systems from legacy connectivity to modern, AI-ready lakehouses. Partner directly with customer IT teams on-site to design ingestion pipelines, configure governance, and develop reusable technical accelerators.
Architect and own the end-to-end design of the data platform, establishing engineering standards for quality, observability, and governance. Partner with leadership to translate business complexity into a technical roadmap that supports underwriting, finance, and AI initiatives.
The Clinical Data Engineer manages all aspects of clinical data integration, maintenance, and reporting to support production services. This includes developing automated solutions for the Enterprise Data Warehouse and ensuring compliance with healthcare data standards.
Own the technical architecture and roadmap of the data platform, balancing scalability, cost, and reliability. Lead complex cross-functional initiatives and mentor senior engineers to raise overall engineering quality.
Design and optimize heat rejection and HVAC systems specifically for data centers, ensuring redundancy and energy efficiency. Conduct CFD analysis and heat load calculations to maintain optimal cooling and air quality for critical IT equipment.
Design, develop, and maintain data pipelines and databases to analyze complex healthcare data sets. Collaborate with cross-functional teams to translate business requirements into analytical frameworks and visualizations.
The role acts as a technical bridge between Data Engineering and AI Platform teams to instrument global product events and ensure alignment with Data Enablement standards. Responsibilities include writing schema extensions, coding alongside app teams for integration, and building automation tools to accelerate onboarding.
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Architect and build the foundational data lakehouse and pipelines to transform raw financial data into trustworthy, intelligence-ready datasets. Establish data quality, observability, and security standards within a regulated environment to enable AI/ML and analytics.
Lead the design and implementation of a modern AWS and Databricks Lakehouse platform to drive enterprise analytics strategy. Manage the full ETL/ELT lifecycle while mentoring a small team and ensuring data reliability and governance.
Design and implement electrical systems for hyperscale and colocation data centers, including power distribution and backup systems. Develop electrical schematics, calculations, and drawings while ensuring compliance with industry standards and codes.
Design and implement electrical systems for hyperscale and colocation data centers, including power distribution and backup systems. Develop electrical schematics, calculations, and drawings while ensuring compliance with industry standards and codes.
Design and implement electrical systems for hyperscale and colocation data centers, including power distribution and backup systems. Develop electrical schematics, calculations, and drawings while ensuring compliance with industry standards and codes.
Lead the design and implementation of robust ETL pipelines to process large-scale customer datasets for Distribution Center products. Define the technical vision for data architecture while mentoring engineers and collaborating cross-functionally to improve data quality.
Build and maintain secure, scalable data systems and pipelines that enable AI applications to access trusted internal data. Partner with analytics, marketing, and product teams to define infrastructure and governance standards for AI-driven experiences.
Drive the architecture and execution of high-impact engineering initiatives to deploy AI/ML models for network traffic analysis. Lead cross-functional technical initiatives and provide mentorship to software engineers to foster engineering excellence.
The Data Engineer II is responsible for driving data transformation projects, including managing ingestion pipelines and optimizing the Snowflake data platform. They will ensure seamless data flow across the enterprise while supporting data governance and financial regulatory compliance.
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Design and maintain a cloud-native data lakehouse using Medallion architecture to deliver trusted data from ingestion to analytics. Implement data quality observability, automated validation, and scalable ETL/ELT pipelines within the Azure ecosystem.
Design and optimize heat rejection and HVAC systems specifically for data centers, ensuring redundancy and energy efficiency. Conduct CFD analysis and heat load calculations to maintain optimal cooling and airflow for critical IT equipment.
Architect and maintain large-scale Spark-driven data pipelines and platforms to enable advanced automation and analytics. Develop AI-augmented tooling, including MCP servers and AI agents, to accelerate data engineering workflows.
Develop and maintain SQL-based ETL pipelines to extract and transform data from enterprise warehouses. Collaborate with business groups to create semantic models and views in Snowflake for advanced analytics and AI modeling.
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