ABOUT THE TEAM

The People Analytics team is at the forefront of transforming how we understand and optimize our workforce. We are a dynamic group of analytical thinkers and storytellers who leverage advanced analytics and robust methodologies to create strategic insights from complex HR data. Collaborating closely with HR Business Partners, Program Management Teams, Business Operations, Talent Acquisition and various cross functional stakeholders we provide actionable insights that drive informed decisions across the entire employee lifecycle; from optimizing talent acquisition and development to enhancing engagement and retention. Our work directly shapes a thriving employee experience, fuels organizational growth, and ensures our people strategy is truly data-driven.

ABOUT THE JOB

This role is central to our People Data & Analytics team, where you will be instrumental in building and maintaining the robust data infrastructure that powers our strategic insights. You'll own the full data lifecycle, from ensuring accurate ingestion and integration of diverse HR data sources, to designing, developing, and optimizing data models and pipelines. Your primary objective will be to transform raw, disparate information into clean, reliable, and analytics-ready datasets, empowering our People Analysts and business stakeholders to unlock deeper understanding of our workforce, enhance employee experience, and drive data-driven decision-making. Success in this role requires a strong technical foundation, meticulous attention to data quality, and a passion for crafting efficient data solutions.

WHAT YOU'LL DO

• Design, build, and optimize robust ETL/ELT pipelines to reliably ingest, integrate, and transform diverse people data from various HR systems (HRIS, ATS, LMS, etc.) into our data platform.
• Develop, maintain, and govern scalable and secure data models, schemas, and ontologies specifically for people analytics, ensuring data quality, consistency, and accessibility for downstream consumption.
• Contribute to the strategic design, development, and evolution of our people data platform and tooling, advocating for engineering best practices, automation, and a scalable analytics ecosystem (e.g., leveraging SQLMesh, Iceberg, Flyte).
• Partner closely with People Analysts, HR Business Partners, and other stakeholders to understand their analytical needs and translate them into robust data solutions, providing well-structured, documented, and reliable datasets.
• Implement and monitor data quality checks, identify discrepancies, troubleshoot data issues, and ensure the reliability and integrity of people data across all systems.
• Continuously monitor the performance of data pipelines and models, identifying bottlenecks and implementing solutions to ensure the efficiency and scalability of our people data infrastructure.
• Create and maintain comprehensive documentation for data pipelines, models, and processes, and champion data engineering best practices (e.g., version control, testing, CI/CD) within the team.
• Implement and enforce strict data security measures and ensure all data handling practices comply with internal policies and external regulations (e.g., GDPR, CCPA) related to employee data privacy.
• Collaborate with broader enterprise analytics and data engineering teams to align on data architecture standards, integrate people data with other business domains, and contribute to the overall evolution of the company's data platform.

REQUIRED QUALIFICATIONS

• 5-7 years of progressive experience in Data Engineering, Analytics Engineering, or a similar role focused on building and optimizing data pipelines and data infrastructure.
• Expert-level proficiency in SQL for complex data manipulation and querying, and advanced Python for scripting, data processing, and automation.
• Extensive experience with cloud-based data warehousing solutions (e.g., Snowflake, Google BigQuery, AWS Redshift, Databricks/Delta Lake) and data lake technologies (e.g., AWS S3, Azure Data Lake Storage).
• Deep understanding and proven experience in designing, implementing, and maintaining robust data models (e.g., dimensional modeling, Kimball methodology) for analytical purposes.
• Hands-on experience building and optimizing complex ETL/ELT processes and data pipelines using modern tools such as dbt, Apache Airflow, Flyte, Dagster, or similar orchestration platforms.
• Excellent communication skills, both written and verbal, with the ability to translate technical concepts for non-technical stakeholders and collaborate effectively across diverse teams.
• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field.
• Strong technical foundation in data engineering, with expertise in SQL, Python, and experience working with cloud-based data platforms (e.g., AWS, GCP, Azure).

Preferred Qualifications:

• Experience with big data processing frameworks (e.g., Apache Spark, Flink) and advanced data warehousing features like schema evolution or time travel (e.g., Apache Iceberg, Delta Lake).
• Direct hands-on experience with tools mentioned in our stack like Palantir Foundry, SQLMesh, Flyte, or similar cutting-edge data orchestration and transformation platforms.
• Relevant professional certifications from major cloud providers (e.g., AWS Certified Data Analytics - Specialty, Google Cloud Professional Data Engineer).
• Experience with tools like Terraform, CloudFormation, Docker, or Kubernetes for managing data infrastructure and deploying applications.
• Familiarity with integrating data pipelines with leading BI tools (e.g., Tableau, Power BI, Looker) to optimize dashboard performance and data accessibility for end-users.
• Deep expertise working with data from specific enterprise HRIS systems like Rippling, Workday, and Oracle HCM Cloud including their data models and APIs.
• Solid understanding of HR data concepts, metrics, and common HR systems (HRIS, ATS, LMS), with a strong interest in People Analytics.

About the Role

Iterative Health is looking for a Sr Manager, Analytics to be the primary analytics partner to our operations and go-to-market teams. You will own how we define, measure, and improve performance across the business, turning raw data into clear insights that drive smarter decisions and faster execution.

This is a hands-on, high-impact role for someone who loves digging into data, building scalable reporting, and influencing strategy. You’ll work closely with Operations, Sales, and our Finance team to ensure leaders have the metrics and insights they need to grow efficiently.

Responsibilities

Own our metrics

• Define and standardize KPIs across sales, marketing, professional services and product
• Establish trusted definitions and a single source of truth for performance data
• Ensure teams are aligned on what “good” looks like and how it’s measured

Deliver insights that drive decisions

• Conduct deep-dive and ad hoc analyses to identify trends, bottlenecks, and growth opportunities
• Translate business questions into structured analyses and actionable recommendations
• Support planning, prioritization, and leadership reviews with clear data storytelling

Build scalable reporting

• Design and maintain dashboards and automated reporting in SQL and BI tools
• Replace manual spreadsheets with reliable, self-serve analytics
• Make data accessible and easy to use for non-technical stakeholders

Forecast and model performance

• Develop forecasts and models for sales performance, capacity planning, and operational targets
• Run scenario analyses to evaluate trade-offs and risks
  
  

Partner on data infrastructure

• Work closely with the Business Intelligence team on reporting architecture and analytics tooling
• Collaborate with Finance, who owns the Enterprise Data Warehouse, to ensure clean and reliable upstream data
• Improve data quality, documentation, and governance practices

Raise the bar on analytics

• Mentor teammates on best practices and enable self-serve reporting
• Champion a metrics-driven culture across the organization

What we’re looking for

Required Qualifications

• 6–9+ years in data analytics, business intelligence, or similar roles in a fast-paced or high-growth environment
• Advanced SQL and comfort working directly with large, messy datasets
• Strong experience with BI tools (Power BI, Looker, Tableau, etc.)
• Proven track record building dashboards, defining KPIs, and automating reporting
• Experience translating ambiguous business problems into structured analyses
• Excellent communication skills - able to influence leaders and explain insights simply
• Highly self-directed and comfortable owning problems end-to-end
  
  

Preferred Qualifications

• Experience with Salesforce or sales/revenue analytics
• Python or R for advanced analysis
• Experience working with modern data warehouses (Snowflake, BigQuery, Redshift)

FEQ227R119

As a Specialist Solutions Architect (SSA) - Data Warehousing, you will guide customers in their cloud data warehousing transformation with Databricks which span a large variety of use cases. You will be in a customer-facing role, working with and supporting Solution Architects, that requires hands-on production experience with large-scale data warehousing technologies and lakehouse architecture. SSAs help customers through evaluations and successful production planning for their business intelligence workloads while aligning their technical roadmap for the Databricks Data Intelligence Platform. As a deep go-to-expert reporting to the Specialist Field Engineering Manager, you will continue to strengthen your technical skills through mentorship, learning, and internal training programs and establish yourself in the data warehousing specialty - including performance tuning, data modeling, winning evaluations, architecture design, and production migration planning.

The impact you will have:

• Provide technical leadership to guide strategic customers to successful cloud transformations on large-scale data warehousing workloads - ranging from evaluation to architecture design to production deployment
• Prove the value of the Databricks Intelligence Platform for customer workloads by architecting production workloads, including end-to-end pipeline load performance testing and optimization
• Become a technical expert in an area such as data warehousing evaluations or helping set up successful workload migrations
• Assist Solution Architects with more advanced aspects of the technical sale including custom proof of concept content, estimating workload sizing and performance, and tuning workloads for production
• Provide tutorials and training to improve community adoption (including hackathons and conference presentations)
• Contribute to the Databricks Community

What we look for:

• 5+ years experience in a technical role with expertise in data warehousing - such as query tuning, performance tuning, troubleshooting, data governance, debugging MPP data warehouses or other big data solutions, or migration workloads from EDWother systems
• Experience with design and implementation of data warehousing technologies including relational databases, SQL, data analytics, NoSQL, MPP, OLTP, and OLAP
• Deep Specialty Expertise in at least one of the following areas:
• Experience scaling large analytical data workloads in the cloud that are performant and cost-effective
• Maintained, extended, or migrated a production data warehouse system to evolve with complex needs, including data modeling, data governance needs, and integration with business intelligence tools
• Experience migrating on-premise EDW workloads to the public cloud
• Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent experience through work experience
• Production programming experience in SQL and Python, Scala, or Java
• Experience with the AWS, Azure, or GCP clouds
• 2 years professional experience with data warehousing and big data technologies (Ex: SQL, Redshift, SAP, Synapse, EMR, OLAP & OLTP workloads)
• 2 years customer-facing experience in a pre-sales or post-sales role
• Can meet expectations for technical training and role-specific outcomes within 6 months of hire
• Can travel up to 30% when needed

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

About Toast

At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement. 

The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations.

Role Overview

You'll be a founding member of a newly chartered data engineering function within Customer Success, with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one. This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership.

As an Analytics Engineer on the Customer Success Data & Analytics team, you'll bridge the gap between raw data and business-ready insights. You'll build the semantic layer, dbt models, and analytics datasets that power reporting, dashboards, and AI-driven workflows across the CS organization. Reporting to the Director of Data Infrastructure & Engineering, you'll work closely with Data Engineers, Analysts, and CS operations leaders to ensure data is not just available but trusted, consistent, and decision-ready.

This is a hands-on role focused on data modeling, metrics standardization, and analytics infrastructure, with a direct line to CS outcomes like customer retention, agent performance, and proactive customer engagement.

A day in the life (Responsibilities) 

• Build and maintain dbt models that transform raw CS source data from systems like Salesforce, Five9, Intercom, NICE, and LevelAI into clean, analytics-ready datasets across CS domains including Customer 360, case management, omnichannel interactions, and agent performance.
• Design and own the semantic and metrics layer for CS KPIs including customer health scores, CSAT, case resolution rates, handle time, and retention signals, ensuring consistent definitions across all reporting surfaces.
• Partner with CS analysts and business stakeholders to translate reporting requirements into reusable, well-documented data models.
• Implement data testing, validation, and observability frameworks to ensure CS data assets are reliable and trustworthy at all times.
• Contribute to self-service analytics enablement by building datasets and semantic models in Snowflake and Sigma/Hex that allow CS analysts and operations leaders to answer questions independently.
• Collaborate with Data Engineers to ensure upstream pipeline outputs are modeled correctly and fit for purpose for analytics use cases.
• Establish and enforce data modeling standards, naming conventions, and documentation practices that scale as the team grows.
• Support AI and ML use cases by building clean, structured feature datasets for customer health scoring, churn prediction, and agent assist models.
• Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards.
• Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements.
• Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations.
• Maintain documentation and metadata for all data assets in accordance with established governance processes.

What you'll need to thrive (Requirements)

• 5 or more years of experience in analytics engineering, data engineering, or a related role with a strong focus on data modeling and analytics infrastructure.
• Expert-level SQL and hands-on experience with dbt for building and maintaining data models in a cloud data warehouse environment.
• Experience with Snowflake or a comparable modern data warehouse such as BigQuery or Redshift.
• Familiarity with BI and analytics tools such as Sigma, Hex, Tableau, or Looker.
• Strong understanding of dimensional modeling, metrics layers, and analytics-ready dataset design.
• Ability to translate ambiguous business requirements from non-technical CS stakeholders into clean, scalable data models.
• Demonstrated ability to implement data quality testing, monitoring, and documentation as a standard practice.
• Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders.
• Collaborative working style with experience partnering across Data Engineering, Analytics, and business operations teams.
• Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems.
  
  

What will help you stand out (Non-essential Skills/Nice to Haves)

• Experience building and optimizing data systems that operate at significant scale, managing billions of records across multiple data domains and systems, with the foresight to design for 5-year growth and future platform scale.
• Comfort using AI-assisted development tools such as GitHub Copilot, Claude, or Snowflake Cortex AI to accelerate pipeline development, testing, and documentation, and curiosity about how GenAI can enhance data product quality.
• Experience with real-time data streaming using Kafka, Kinesis, or Pub/Sub.
• Exposure to data governance, metadata management, and observability tools.
• Background in SaaS or Customer Success analytics, including usage data, retention metrics, and customer health.
• Experience working with CS or CX-adjacent source systems such as Salesforce, Five9, Intercom, or similar CCaaS/CRM platforms, and familiarity with the data models and integration patterns they produce.
• Knowledge of data security, privacy, and compliance in cloud environments.
• Experience with CI/CD, infrastructure as code, or containerization such as Docker or Terraform.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy

We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.

About Toast

At Toast, we're building the restaurant platform that helps restaurants adapt, take control, and thrive. The Customer Success (CS) organization plays a pivotal role in helping customers get the most out of our products and we're transforming our data capabilities to drive a new era of proactive, data-informed customer engagement. 

The CS Data & Analytics team is at the center of this transformation, building the data infrastructure that makes proactive, data-informed customer engagement possible at scale for ~150,000 restaurant locations.

Role Overview

You'll be a founding member of a newly chartered data engineering function within Customer Success with a direct hand in shaping the architecture, tooling, domain model, and team culture from day one. This is a rare opportunity to build something from greenfield, with visibility to VP and senior CS leadership.

As a Data Engineer on the Customer Success Data & Analytics team, you’ll design, build, and scale the data platforms and pipelines that power insights across our Customer Success organization. Reporting to the Director of Data Infrastructure & Engineering, you’ll work closely with Data Infrastructure, Analytics, Engineering, and Product teams to ensure high-quality, reliable data is accessible and actionable.

This is a hands-on engineering role focused on delivering production-grade, scalable, and reliable data systems that support analytics, reporting, and operational decision-making across Customer Success.

A day in the life (Responsibilities) 

• Design, build, and maintain scalable, production-ready data pipelines (batch and real-time) for Customer Success using modern data technologies.
• Develop and evolve data models and schemas that enable reporting, analytics, and machine learning across Customer Success functions.
• Partner with CS operations leaders across care, workforce, and CX to translate business requirements into pipeline and infrastructure priorities, ensuring data work is directly connected to customer retention and operational outcomes.
• Ensure data quality, observability, lineage, and governance are built into every data workflow.
• Implement best practices for cost-efficient, performant cloud-based data infrastructure.
• Contribute to data platform tooling and frameworks that support self-service analytics and reusability across teams.
• Actively monitor and optimize data workflows for reliability, scale, and maintainability.
• Collaborate with cross-functional stakeholders to define and track key metrics and ensure consistent data definitions.
• Participate in code reviews, design discussions, and technical architecture planning to continually raise engineering standards.
• Build and maintain semantic and metrics layer foundations (e.g., dbt metrics, Snowflake semantic layer) that enable consistent, reusable definitions of CS KPIs across reporting and analytics surfaces.
• Contribute to the design and evolution of the CS data domain model including Customer 360, omnichannel interaction data, case management, and agent performance in close partnership with cross-functional stakeholders.
  
  

Governance & Standards

Partner with Toast's Business, Technology & Transformation (BTT) organization to establish data governance practices, security policies, and architecture standards that will serve as the foundation for CS data infrastructure. As a founding team member, you'll help define the norms, not just follow them.

• Ensure data pipelines, storage, and access patterns adhere to BTT data standards, security policies, and compliance requirements.
• Partner with BTT governance forums to align on design patterns, data architecture decisions, and SLA expectations.
• Maintain documentation and metadata for all data assets in accordance with established governance processes.
  
  

What you'll need to thrive (Requirements)

• 5+ years of experience in data engineering, software engineering, or a related role building and maintaining data systems in production.
• Strong SQL skills and experience with a programming language such as Python, Scala, or Java.
• Hands-on experience with cloud-based data platforms (AWS, GCP, or Azure).
• Experience building and optimizing ETL/ELT workflows using tools such as Airflow, dbt, or similar orchestration frameworks.
• Deep understanding of data modeling, data warehousing, and pipeline design.
• Ability to write clean, maintainable, and well-tested code for data transformations and automation.
• Demonstrated ability to communicate technical tradeoffs and data concepts clearly to non-technical stakeholders, including operations and business leaders.
• Proven ability to work collaboratively with analytics, operations, and engineering teams.
• Strong problem-solving skills, attention to detail, and a drive to build scalable, reliable systems.
  
  

What will help you stand out (Non-essential Skills/Nice to Haves)

• Experience building and optimizing data systems that operate at significant scale, managing billions of records across multiple data domains and systems, with the foresight to design for 5-year growth and future platform scale.
• Comfort using AI-assisted development tools (e.g., GitHub Copilot, Claude, Snowflake Cortex AI) to accelerate pipeline development, testing, and documentation and curiosity about how GenAI can enhance data product quality.
• Experience with real-time data streaming (Kafka, Kinesis, or Pub/Sub).
• Familiarity with modern data warehouse/lakehouse technologies (e.g., Snowflake, BigQuery, Redshift, Delta Lake).
• Experience with CI/CD, infrastructure as code, or containerization (e.g., Docker, Terraform).
• Exposure to data governance, metadata management, and observability tools.
• Background in SaaS or Customer Success analytics (usage data, retention metrics, customer health).
• Experience working with CS or CX-adjacent source systems such as Salesforce, Five9, Intercom, or similar CCaaS/CRM platforms and familiarity with the data models and integration patterns they produce.
• Knowledge of data security, privacy, and compliance in cloud environments.

AI at Toast

At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.

Our Total Rewards Philosophy

We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at https://careers.toasttab.com/toast-benefits.

WHAT WE DO

Our Data and Analytics consultants work with clients at all levels of the organization, from the C-suite to the shop floor, helping them to deliver on their most strategic initiatives. We’re known for making realistic, data-driven decisions that deliver value in tangible ways to our clients. Our clients ask for us on projects that require a superior combination of technical and business capabilities, people and management skills, and a collaborative mindset. We excel in understanding complex programs and strategic initiatives and breaking them into actionable pieces.

We are actively looking for professionals in the following areas:

• Data Strategy
• Data Governance
• Data Modernization
• Advanced Analytics
• Data Visualization

The ideal candidate’s experience may include but is not limited to the following:

• Have experience understanding and solving real business problems
• Have experience with presenting business case and strategy to the C-Suite
• Bring industry domain knowledge for custom data and analytics strategies
• Experience in designing and implementing data driven products and services
• Experience in designing and building high performance collaborative business and technology teams that deliver high value data solutions, data products and data analytics

Ideal candidates may call themselves Data Architects, Data Engineers, Data Scientists, Analysts and Data Governance professionals. Experience may include but not limited to the following:  

Data Strategy and Governance

• Intimately familiar with data technologies, data architecture frameworks, cloud platforms and the emerging trends in technological advancements.
• Proven experience in transforming our clients from on-prem data platforms to the cloud.
• Lead data modernization initiatives that include platform, technology and tools implementation and adoption.
• Must have experience in architecting and implementing data architecture, data engineering, reporting and analytical solutions across multiple business functions and domains.
• Conduct data management maturity assessments and identify pain points for including data quality, governance, architecture, analytics, metadata management, master data management.
• Demonstrate a rigorous and analytical problem-solving approach, leading information-gathering sessions and preparing and delivering client presentations and work products/prototypes.
• Identify data governance gaps and assess data governance maturity in a complex data environment. Should also be able to Identify the implications for data governance that arise from the technology, current processes, and skill levels in a complex data environment.

Data Architecture and Engineering

• Extensive experience or knowledge of cloud-based data platform technologies such as AWS, Microsoft Azure or Google Cloud Computing.
• Experience designing and building data platforms that support data lakes, data stores, data ingestion and transformations and API services for consuming data
• Ability to design and build complex data pipelines that either stream data in
• Experience and knowledge of programming and scripting languages, such as but not limited to Python, Node.JS and SQL
• Experience and knowledge of data integration tools and platforms
• Experience and knowledge of relational and dimensional database structures, theories, principles, and practice used in data warehousing and analytics solutions
• Ability to design and build data models using the appropriate technics to meet business objectives
• In depth knowledge of modern data technologies including Snowflake, RedShift, Azure SQL/Synaspe, Databricks or similar technologies.

Advanced Analytics and Data Visualizations

• Extensive experience or knowledge of data visualization technologies such as Tableau, Power BI, Qlik and Spotfire
• Experience with statistical and mathematical modeling, artificial intelligence and machine learning software and methods
• Experience and knowledge of programming and scripting languages such as, but not limited to, SQL, Python and R
• Experience with data modeling, data prep and machine learning tools like Alteryx, RapidMinder, RStudio and Tableau Prep
• Experience and knowledge of relational and dimensional database structures, theories, principles, and practice used in data warehousing and analytics solutions
• Experience deploying enterprise-wide reporting solutions while leveraging data visualization best practices

A career at SEI extends well beyond providing great service and thought leadership to our clients. Everyone takes an active role in building and managing our business, in an environment that runs counter to traditional consulting firms. Our consultants have a “seat at the table” and contribute to growing our business in ways that align to their interests such as growing business development opportunities, conducting interviews to support our hiring process, managing internal initiatives that build our brand or organizing trainings to share what you know with your colleagues. There is no telling what an SEI Consultant will be asked to do on a day-to-day basis – we do what it takes to get the job done.

QUALIFICATIONS

Required-

• Alignment to our core values: Excellence, Participation, Integrity and Collaboration
• Hungry, Humble, Smart
• Demonstrated business and technology acumen
• Strong written and verbal communication skills
• Understanding and experience solving real business problems
• Proven track record of delivering results
• Experience working with and/or leading a team
• Ability to work across industries, roles, functions & technologies
• Authorization for permanent employment in the United States (this position is not eligible for immigration sponsorship)

Preferred-

• Bachelor’s degree
• 8+ years professional experience
• Experience across our service offerings