Role Description As the Healthcare Consulting Associate in Mid Revenue Cycle Analytics, you will: - Design, develop, and execute healthcare data analyses supporting mid‑revenue cycle consulting services, including Service Capture, Clinical Documentation & Coding, and Managed Care. - Apply critical thinking and advanced analytical techniques across multiple data sources to identify performance trends, diagnose issues, uncover risks, and develop sustainable, implementable recommendations. - Perform centralized, repeatable analytical processes including data mapping, validation, standardization, quality assurance, and ad‑hoc querying across complex datasets. - Extract, analyze, and interpret healthcare data from Huron’s analytics platform to generate actionable performance insights. - Design and build data visualizations, dashboards, and standardized reports to support consulting delivery and client decision‑making. - Package analytical insights into clear, client‑ready deliverables, effectively presenting findings, risks, and recommendations to varying levels of Huron and client leadership. - Contribute to the development of next‑generation metrics, insights, and analytical capabilities within Huron’s analytics platform. - Partner with functional, technical, and analytics stakeholders to implement new technologies and enhance analytical processes. - Deliver analytics‑enabled solutions tailored to each client’s unique needs, improving impact and accessibility across healthcare services. Qualifications - Bachelor’s degree required. - 3+ years of relevant data analytics preferably within healthcare or consulting. - 3+ years of healthcare experience, preferably within revenue cycle, reimbursement, or managed care. - Strong SQL experience required, including the ability to join across datasets to enhance and enrich analyses. - Previous exposure to and working knowledge of commercial and government reimbursement methodologies, including but not limited to DRGs, case rates, fee schedules, IPPS, OPPS, and related payment models. - Experience interpreting payer contracts, including understanding reimbursement hierarchies and key contractual terms. - Proven analytical skills working with large, complex datasets. - Demonstrated ability to juggle multiple priorities and project assignments in a dynamic consulting environment. - Strong problem‑solving and critical‑thinking skills. - Demonstrated ability to communicate complex analytical and technical information clearly and concisely to internal stakeholders and, as needed, client audiences. - Proficiency in Microsoft Office (Word, PowerPoint, Excel). - U.S. work authorization required. Requirements - Experience working with comprehensive healthcare datasets, including claims, reimbursement, financial performance, and clinical data. - Recent experience in a team‑based professional services, consulting, analytics, or technical environment. - Experience working in a matrixed organization or cross‑functional team environment. Benefits - Estimated base salary range: $100,000 - $145,000. - Eligible to participate in Huron’s annual incentive compensation program. - Total estimated compensation range (including incentives): $112,000 - $171,100. - Eligible to participate in Huron’s benefit plans, including medical, dental, and vision coverage and other wellness programs.

• Maintain and expand the company’s analytical data model using Snowflake and dbt, ensuring datasets are reliable, well-structured, and easy to use. • Partner with stakeholders to understand reporting and analytics needs and translate them into new models and datasets. • Investigate discrepancies in metrics and datasets and perform root-cause analysis across systems. • Monitor and maintain ELT pipelines across our data stack. • Investigate and resolve pipeline failures, schema changes, and data inconsistencies. • Identify opportunities to improve pipeline reliability, efficiency, and cost effectiveness. • Expand documentation across the data model to clearly describe business logic, relationships, and definitions. • Ensure datasets are clearly structured and documented so they can be reliably used across analytics tools and internal workflows. • Contribute to the structured AI data context files that help internal AI tools accurately interpret datasets and metrics. • Help maintain data governance standards, including contributing to PII masking policies and ensuring sensitive customer data is handled appropriately across the data platform. • Work closely with Product, Revenue, and Operations teams to understand their data needs and questions. • Help stakeholders navigate the data model and identify the most appropriate datasets for their use cases. • Occasionally build or modify Hex projects to support data exploration or reporting needs.

Senior Analytics Engineer (Platform - Financial Analytics) Remote - USA Ready to be pushed beyond what you think you’re capable of? At Coinbase, our mission is to increase economic freedom in the world. It’s a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform — and with it, the future global financial system. To achieve our mission, we’re seeking a very specific candidate. We want someone who is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up. We want someone who will run towards, not away from, solving the company’s hardest problems. Our work culture is intense and isn’t for everyone. But if you want to build the future alongside others who excel in their disciplines and expect the same from you, there’s no better place to be. While many roles at Coinbase are remote-first, we are not remote-only. In-person participation is required throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported. About the Team The Finance Analytics team bridges the gap between data engineering, data science, and business analytics by building scalable, impactful data solutions that empower Finance, Accounting/Controllership, and Treasury stakeholders to make data-driven decisions. We bring deep domain knowledge spanning accounting, business controllership, SOX compliance, and internal audit - ensuring our pipelines, data models, and certified financial datasets meet the rigor and traceability demands of a regulated financial environment. This role supports the data layer efforts that underpin these datasets and the controls/observability needed to keep them reliable. The team partners closely with Finance, Accounting, Treasury, and Product Engineering to ensure financial data is complete, reconciled, and audit-ready - supporting core workflows like month-end close, safeguarding, and reporting at scale. What You'll Be Doing: This is a hybrid Data Engineer/Data Scientist/Business Analyst role that has the expertise to understand data flows end to end, and the engineering toolkit to extract the most value out of it indirectly (building tables) or directly (solving problems, delivering insights). - Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery. - Step into a new line of business and work with Engineering and Product partners to deliver first data pipelines and insights. - Communicate with engineering teams to fix data gaps for downstream data users. - Take initiative and accountability for fixing issues anywhere in the stack. - Perform reconciliation-style validation across sources (internal systems and/or external statements/vendors), identifying discrepancies and driving fixes with upstream owners. - Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly). - Build curated data models that streamline ledger verification and accounting workflows, helping finance teams accelerate time-to-close for new product launches. - Leverage deep understanding of the reconciliation engine alongside statistical and data expertise to propose engineering improvements that drive faster execution and higher match accuracy. - Work with PMs to tie together new x-PG, and x-Product data into one holistic framework to optimize key financing product business metrics. - Collaborate cross-functionally with Finance/Accounting to translate requirements into durable data models, and with upstream engineering teams to improve source data contracts. - Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value. - Develop new abstractions (e.g. UDFs, Python packages, dashboards) to support scalable data workflows/infra. - Stand up a framework for debugging AI skills/data apps internally, enabling other non-tech stakeholders to quickly add value. - Use established tools with mastery (e.g. Google Sheets, SQL) to quickly deliver impact when speed is top priority. - Ensure financial correctness & reliability: Implement strong data quality guarantees (tests, monitoring, alerting, SLAs) and partner with stakeholders to define "done" for financial correctness. Improve reliability and operational excellence for critical pipelines (incident response, retro/action items, performance tuning, cost optimization). What We Look For in You: - Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas, snowflake schemas). Experience designing curated datasets for analytics/reporting with clear definitions and change management. - Proficiency in advanced SQL techniques for data transformation, querying, and optimization. - Expertise in scripting and automation, with experience in Object-Oriented Programming (OOP) and building scalable frameworks. - Strong ability to translate technical concepts into business value for cross-functional stakeholders. Proven ability to manage projects and communicate effectively across teams. - Strong cross-functional communication skills and ability to work effectively with Finance/Accounting partners and navigate ambiguity. - Experience building, maintaining, and optimizing ETL/ELT pipelines, using modern tools like dbt, Airflow, or similar. Experience orchestrating data workflows with Airflow (DAG design, scheduling patterns, backfills, operational ownership). - Proficiency in building polished dashboards using tools like Looker, Tableau, Superset, or Python visualization libraries (Matplotlib, Plotly). - Familiarity with version control (GitHub), CI/CD, and modern development workflows. - Knowledge of modern data lake/warehouse architectures (e.g., Snowflake, Databricks) and transformation frameworks. Hands-on experience with Snowflake and/or Databricks in production environments. - Track record of building for correctness and reliability: data quality frameworks, monitoring/alerting, incident response, and stakeholder-facing SLAs. - Ability to understand and address business challenges through analytics engineering. - Familiarity with statistics and probability. - Expertise in prompt engineering and design for LLMs (e.g., GPT), including creating, refining, and optimizing prompts to improve response accuracy, relevance, and performance for internal tools and use cases. - Demonstrate the ability to responsibly use generative AI tools and copilots (e.g., LibreChat, Gemini, Glean) in daily workflows, continuously learn as tools evolve, and apply human-in-the-loop practices to deliver business-ready outputs and drive measurable improvements in efficiency, cost, and quality. Nice to Haves: - Experience with financial reconciliation, controllership/accounting reporting, audit/SOX-style controls, or regulated environments. - Familiarity with ledger/event-based financial models and concepts like double-entry accounting. - Experience with streaming/event-driven systems (e.g., Kafka/Kinesis) and/or near-real-time data validation patterns. - Experience with table replication/synchronization patterns between lakehouse and warehouse environments. - Fintech/crypto domain experience. - New York City location presence preferred. The role is remote-friendly but we prefer candidates who can commute to our NYC office for stakeholder meetings and project work as needed. Job ID: P76160 Pay Transparency Notice: Depending on your work location, the target annual base salary for this position can range as detailed below. Total compensation may also include equity and bonus eligibility and benefits (including medical, dental, vision and 401(k)). Annual base salary range (excluding equity and bonus): $180,370 - $212,200 USD Please be advised that each candidate may submit a maximum of four applications within any 30-day period. We encourage you to carefully evaluate how your skills and interests align with Coinbase's roles before applying.

Role Description Phillips & Cohen Associates Ltd. is seeking an Analytics Engineer to support the Strategy & Analytics function and help identify opportunities that drive performance. The ideal candidate should demonstrate strategic curiosity, challenge assumptions, and translate analysis into insights. This role combines analytics engineering, data modeling, automation, and applied analytics to transform data into actionable insights, connecting data analysis to execution to accelerate growth and competitive advantage. The Analytics Engineer plays a critical role in turning data into a strategic asset, ensuring the business can rely on accurate, timely insights to drive performance and growth. - Support analytics use cases (segmentation, forecasting, A/B testing). - Develop, implement, and optimize debt recovery processes, including dialer, scoring, and portfolio segmentation. - Engineer new data assets and derived datasets when required information is not available in existing source systems. - Develop scalable data models and schemas to support portfolio valuation, forecasting, reporting, and operational analytics. - Eliminate conflicting metrics and definitions, creating a single source of truth across teams. - Build and maintain automated end-to-end analytics workflows, including scheduling, orchestration, alerts, and reporting. - Implement data quality rules, validation logic, automated QA checks, and monitoring frameworks to ensure data integrity. - Perform exploratory data analysis to identify trends, data gaps, and metric behavior related to portfolio performance. - Translate business questions and strategy needs into technical requirements, datasets, and analytical solutions. - Create reports, dashboards, and analytical outputs that are usable and interpretable by non-technical stakeholders. - Optimize SQL queries and ETL/ELT processes for performance, accuracy, and reliability. - Collaborate closely with analytics, engineering, and business partners to align data definitions, metrics, and logic. - Document data pipelines, models, transformations, business rules, and data dictionaries to support governance and continuity, ensuring consistent metric definition across the organization. Qualifications - Minimum of 3 years of experience in analytics engineering, data engineering, data analytics, or similar hybrid analytics roles. - Advanced SQL skills with hands-on experience building scalable transformations and data models. - Proficiency in programming languages such as SAS, Python, or R for analytics, automation, and modeling. - Demonstrated experience designing datasets and data architecture to support advanced analytics and forecasting. - Experience with BI and visualization tools such as Power BI, Tableau, or Looker. - Strong analytical thinking with the ability to translate complex data into actionable business insights. - Ability to work effectively in ambiguous, open-ended problem spaces. - Understanding of statistical concepts and predictive modeling techniques. - Strong written and verbal communication skills with both technical and non-technical audiences. - Experience developing SQL Server jobs, stored procedures, triggers, or similar database objects. - Familiarity with Git or version control systems. - Background in non-performing debt industry, financial services, portfolio analytics, forecasting, or operations analytics is a plus.