Data Engineer

• Design and implement robust ETL/ELT pipelines
• Build and maintain Feature Stores and specialized datasets
• Develop the data pipelines required for Generative AI
• Act as the technical lead for our Snowflake data warehouse
• Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory
• Partner directly with central IT, Database Administrators, and Security teams
• Implement automated validation and observability layers
• Drive the efficiency of our data stack by optimizing storage
• Work as a dedicated engineering partner to MLOps and Data Science teams

Data Engineer, AI

• Serves as a lead in the analysis of business requirements associated with the design and implementation of highly complex, highly scalable enterprise data processes
• Designs and develops robust and efficient processes for extracting, loading and transforming data to meet the needs of enterprise scale projects
• Provides guidance and participates in activities involved in the analysis, design, development, troubleshooting, debugging and maintenance of an enterprise data management and data ecosystem
• Identifies, investigates, tracks, and resolves the most complex technical errors ensuring the solution is timely and of acceptable quality
• Establishes and enforces database standards, guidelines, procedures and quality assurance for database deliverables such as conceptual design, logical data model capacity planning, external data interface specs, data loading, data maintenance plan and security
• Reviews and analyzes data objects and models to improve data management and data access
• Designs, constructs, tests and implements extraction and transformation programs, processes and cycles for historical and current source data
• Creates technical specification for supporting enterprise data transformation processes and supplementing system documentation for ETL processes
• Performs data profiling, source-target mappings, data transformation development, optimization, testing and implementation
• This position participates and serves as a lead in maintaining the ETL environment for the company’s system-wide enterprise data warehouse.

Supply Chain Data Engineer

• support the development and maintenance of data pipelines
• partner closely with data engineers, business analysts, and stakeholders
• participate in an on-call rotation
• design, develop, modify, or integrate information systems

Associate Data Engineer

• Build and optimize data pipelines that power critical system and vendor integrations
• Partner closely with data engineers, business analysts, vendors, and stakeholders
• Troubleshoot scalable data pipelines and support data warehouse structures
• Participate in an on-call rotation and collaborate across teams

Associate Data Engineer II

• Oversee all aspects of data platform operations, including: SDLC adherence, Change management, Financial governance, Vendor coordination, Cloud cost optimization, Technology lifecycle activities
• Collaborate with Engineering, Product, Security, Compliance, and Business teams to ensure technology solutions are delivered on time, within budget, and in alignment with organizational goals

Manager, Data Engineering

• Design, build, and maintain scalable data pipelines and APIs on Google Cloud Platform.
• Develop automated workflows and data platforms that support analytics, reporting, and AI/ML use cases.
• Implement best practices for data security, governance, CI/CD, and automated deployment.
• Collaborate with data engineers, architects, data scientists, and business stakeholders.
• Produce high-quality, reusable code and mentor team members on best practices.
• Support testing, deployment, monitoring, and production troubleshooting.

Senior GCP Data Engineer

• Design, build and maintain data systems and pipelines
• Ensure data is accessed and utilized optimally for analytics or visualization/insight generation
• Build and execute data engineering approaches to support business priorities
• Integrate varying sizes of data from different sources (including Snowflake, Fineos, Web API and Teradata)
• Create data assets that conform to scalability, extensibility, performance and maintainability requirements for the problem at hand
• Understand and contribute to the evolution of the enterprise data architecture
• Support integration of solutions within existing business processes using automation techniques
• Provide support to lower level Data Engineer peers

Data Engineer I

• Manage a team by hiring and onboarding talent, setting clear expectations, conducting formal performance reviews, and coaching accountability.
• Lead the team to deliver on the enterprise data platform roadmap by translating architecture direction into sequenced execution plans, team backlogs, and measurable outcomes.
• Create a strong engineering culture across the team by setting standards for maintainability, organization, and repeatability; growing engineers via regular 1:1s, timely feedback, development plans, and formal review cycles; and being responsible for overall team performance and cohesion.
• Own day-to-day execution for the Data Engineering team by breaking work into tasks, driving sprint-level planning, delegating effectively to grow ownership, unblocking delivery, and ensuring high-quality outcomes through engineering reviews and feedback.
• Drive implementation of the Medallion architecture (Bronze → Silver → Gold) with strong enforcement of layer responsibilities, ensuring repeatable and auditable pipeline behavior leveraging Boomi, Dbt, Azure Data Bricks, Boomi/Rivery, Delta tables, and Sql Server.
• Partner closely with the Data Architect to bring the architecture to life through concrete implementation patterns, reference pipelines, and guardrails—ensuring the team can consistently execute the intended approach.
• Reduce friction by driving crisp decisions and forward motion—using lightweight decision records, seeking the right input, and then executing accountability (while avoiding overly heavy process).
• Establish DataOps practices aligned with modern software development lifecycle (SDLC): CI/CD for data assets, consistent branching/release patterns, code review standards, and runbooks/operational readiness for pipelines.
• Improve pipeline observability and operational reliability by implementing monitoring for freshness/staleness, failure modes, and quality signals, and ensuring the team responds predictably to incidents and support requests.
• Drive stakeholder partnership and intake stream: collaborate with business partners to clarify requirements, reduce ad-hoc thrash, and shape requests into deliverable, well-scoped work that fits the platform strategy.
• Develop future leaders and succession plans for key roles through assigning regularly occurring responsibilities, mentoring, and strategic delegation.
• Support and promote the company values through positive interactions with both internal and external stakeholders on a regular basis.
• Other strategic business initiatives or special cross-functional project involvement as required.

• Design and implement data architecture and ETL solutions.
• Collaborate with business stakeholders, data engineers, and data scientists to understand data requirements and design scalable and flexible data models.
• Develop data ingestion pipelines and frameworks to efficiently capture and integrate data from various sources into data warehouse platform (on-prem & cloud).
• Define data storage and retrieval mechanisms, including multi-tenant data partitioning, indexing, and compression, to optimize performance and cost efficiency.
• Create and maintain data pipelines and ETL/ELT processes to transform, cleanse, and integrate data across different data sources.
• Perform data profiling, analysis, and troubleshooting to identify and resolve data-related issues.
• Identify technical areas for improvement and present detailed business cases for improvements or new areas of opportunities.
• Work with external and internal teams to resolve infrastructure-related issues when developing integration solutions.
• Work with IT functional leads to understand the application architecture and setup right integration mechanisms.
• Follow DevOps practices across all analytics systems.
• Communicate complex ideas in simple terms, and effectively interact with decision makers.

• support the delivery of high-impact dashboards and reporting solutions used across the organization.
• partner with senior Tableau developers and business stakeholders to help translate data into clear, intuitive visualizations that support decision-making.
• build and enhance Tableau dashboards, assist with data modeling using SQL and cloud data sources such as Google BigQuery, and help support well-governed reporting solutions that can be scaled for broader enterprise use.

• Lead, mentor, and develop a team of data, architectural engineers across onshore and offshore resources
• Support workforce planning, hiring, onboarding, coaching, and performance management
• Provide architectural direction for the enterprise data warehouse and data lake
• Design and optimize scalable, secure data pipelines using Azure Databricks, Apache Spark, and Azure-native data services
• Guide integration of data from multiple healthcare sources, including EMRs, claims systems, and patient portals
• Partner with clinical, operational, analytics, and IT teams to deliver trusted, self-service data and insights
• Oversee strategic data initiatives and projects, ensuring alignment with business priorities and organizational goals
• Establish and enforce data governance, privacy, security, and quality standards across platforms and pipelines
• Stay current with industry trends in data engineering, process automation, and cloud platforms

Manager, Data Engineering – Architecture

• Build and maintain high-performance pipelines using Airflow, dbt, and Python to organize raw data for analysis, modeling, and product use cases
• Partner with Product, Data Science, and ML Engineering to build the infrastructure supporting machine learning workloads, including training datasets, feature pipelines, and model output tables
• Write production-grade SQL (Amazon Redshift) and manage query optimization to support rapidly growing business needs
• Build instrumentation, monitoring, and alerting to guarantee data freshness, quality, and availability in production environments
• Identify and implement improvements by automating manual tasks and reducing pipeline runtimes
• Utilize AI-assisted developer tools (like Claude Code) daily to accelerate development and participate in design reviews and on-call rotations

• Deploy new data pipelines
• Design & build data observability platforms and metrics
• Build metadata driven pipeline solutions
• Monitor and optimize data transformations
• Build third party data integrations

• Design, build, and maintain scalable Snowflake-based data warehouse architectures, ensuring performance, reliability, and extensibility for future growth.
• Own data architecture and technical design decisions, including data models, integration patterns, and optimization strategies.
• Transform raw source data from multiple internal and external systems into analytics-ready data models using industry best practices.
• Lead and contribute to warehouse development using dimensional modeling techniques (e.g., star schemas, slowly changing dimensions).
• Develop and manage data pipelines using Snowflake features such as Snowpipe, tasks, stored procedures, and performance optimization techniques.
• Manage and monitor data integrations with internal systems via APIs, application calls, or custom code to ensure data accuracy and reliability.
• Apply auditing, change tracking, error handling, and monitoring across large datasets to maintain high data quality standards.
• Identify and resolve data quality issues and implement controls to distinguish valid vs. invalid data used in decision-making.
• Leverage Python and application knowledge to interact with and extract data from other systems.
• Provide technical leadership through coaching and mentoring junior and mid-level data engineers, including code reviews, design feedback, and best-practice guidance.
• Contribute to and help define data engineering standards, best practices, and documentation across the team.
• Collaborate across teams to support day-to-day operational data needs and continuously improve data pipelines and processes.

Senior Data Engineer

• Collaborate & Strategize: Partner closely with business stakeholders to understand challenges and design end-to-end architecture.
• Build & Maintain Data Models: Design, develop, and own robust data models in Snowflake and Iceberg using dbt and advanced SQL.
• Orchestrate & Automate: Build reliable data pipelines and CI/CD workflows using tools like Airflow, Python, and Terraform.
• Champion Data Quality: Implement testing, documentation, and data governance practices to maintain a single source of truth.
• Enable Analytics & Workflows: Act as Product Owner and Tech Lead for data domains.
• Innovate with AI: Help build next-generation data infrastructure by integrating AI capabilities.
• Architect Observability: Implement monitoring and alerting frameworks to catch data failures proactively.

• Own End-to-End Delivery of Core Data Platform Components
• Design and ship the data normalization, schema mapping, validation, enrichment, and distribution pipeline for a net-new intelligent data warehouse
• Write production code as a hands-on individual contributor. This is not a role that delegates implementation to others
• Take technical ownership from architecture through deployment, with accountability for reliability, performance, and correctness
• Drive Technical Architecture
• Partner with a small seed team of senior engineers to define the end-to-end architecture for an AI-native data warehouse serving institutional financial clients
• Bring opinionated decisions on schema design, normalization strategies, API exposure patterns, and data distribution approaches
• Evaluate and select technologies with a bias toward what ships well and scales sustainably
• Build AI Evaluation Infrastructure
• Design and implement the evaluation framework that makes AI-generated outputs trustworthy in high-stakes financial data contexts
• Build cross-model comparison tooling, deterministic validation checks, and human-in-the-loop review workflows
• Contribute to shared AI infrastructure that will serve as a foundation for multiple products across the company
• Ship with AI-Native Development Practices
• Use agentic coding tools and LLM-assisted development as your primary workflow; this is how the entire team operates
• Bring strong opinions about how to get the most out of AI-assisted development while maintaining quality and reliability
• Contribute to the team’s evolving practices around AI-accelerated SDLC
• Establish Technical Standards
• As an early senior voice on a new team, set coding standards, review practices, and architectural documentation that will scale
• Help define what “good” looks like for a team building at speed without sacrificing quality
• Mentor and provide technical guidance as the team grows from seed squad to full delivery organization
• Collaborate Across Engineering and Product
• Work closely with your engineering manager, peer engineers, and product management to translate requirements into executable technical plans
• Participate actively in design reviews and roadmap discussions with grounded, implementation-level perspective
• Engage directly with the nuances of private markets data – fund administration, investment data schemas, institutional reporting workflows – to build domain intuition that improves your technical decisions

Staff Software Engineer, Data Platform

• Lead, mentor, and develop engineers across both the Data Operations and Analytics Engineering teams
• Contribute to a culture of clear goals, open feedback, and continuous growth across the team.
• Promote collaboration and accountability while driving high standards for quality, efficiency, and continuous improvement.
• Lead incident management and resolution for pipeline failures, cluster issues, and data quality problems, driving thorough root cause analysis and preventative improvements
• Define and enforce operational standards, runbooks, and on-call practices for the team
• Manage and maintain Databricks Workflows and job orchestration, ensuring SLAs are consistently met
• Oversee the design, development, and maintenance of data models and transformations that serve business intelligence and analytics use cases
• Define and enforce analytics engineering best practices including modular transformation patterns, data testing, and code review standards
• Partner with data analysts and business stakeholders to understand modeling requirements and ensure data is accurate, accessible, and well-understood

Lead Data Engineer

Cloud Data Architect

• Design, build, and maintain scalable data pipelines and APIs on Google Cloud Platform
• Develop automated workflows and data platforms that support analytics, reporting, and AI/ML use cases
• Implement best practices for data security, governance, CI/CD, and automated deployment
• Collaborate with data engineers, architects, data scientists, and business stakeholders
• Produce high-quality, reusable code and mentor team members on best practices
• Support testing, deployment, monitoring, and production troubleshooting

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