Wix

• Own delivery end-to-end — full visibility into timelines, deliverables, risks, dependencies, and owners • Build and maintain clear implementation plans that map customer dates, Attuned deliverables, customer dependencies, and internal next steps • Partner with AI Engineering to configure, tune, and deliver high-quality AI agents for each customer • Serve as the named operational point of contact for customer and partner program asks, making sure inbound requests are acknowledged, triaged, and routed to the right Attuned owner • Identify and resolve issues, chasing internal and external blockers and escalating with GTM and Operations leadership when needed • Own recurring customer meetings (weekly standups, implementation reviews, executive business reviews) — agenda setting, KPI reporting, and follow-through on action items • Own customer health after go-live — tracking adoption, call-volume ramp, and agent performance, and acting on warning signs before they become escalations • Translate platform impact into the outcomes customers care about — calls recovered, appointments booked, wait times reduced — and report them in KPI reviews and executive business reviews • Build the post-launch relationships that drive retention and renewal, serving as the customer's primary operational point of contact and making sure open requests keep moving • Surface expansion signals and partner with Health Account Executives, who own the commercial relationship, to grow each account • Build and refine implementation playbooks as the customer base grows • Build lightweight systems for tracking owners, dates, dependencies, and follow-up across customer and partner programs • Surface product feedback from the field into our product development process • Identify operational gaps and lead process improvements proactively

• Design and maintain enterprise databases, including stored procedures, indexing, views, SQL scripts, and database objects. • Develop and optimize ETL pipelines and workflows using SSIS, Python, PySpark, and SQL Server. • Build data ingestion and transformation workflows across raw, staging, curated, and analytics data layers. • Develop and optimize SQL scripts and stored procedures for data conversion, validation, and ongoing processing. • Execute large-scale data migration projects, including data cleansing, validation, and transformation across enterprise systems. • Design and implement logical and physical data models (ERDs). • Develop database components supporting transactional and reporting systems. • Build and maintain reporting solutions using SSRS and Power BI. • Monitor, troubleshoot, and optimize SQL Server database performance. • Provide production support, including troubleshooting performance issues and participating in on-call support when needed. • Collaborate with developers, business users, QA teams, and project stakeholders. • Participate in Agile ceremonies, development activities, code reviews, testing, and release cycles. • Provide technical documentation and knowledge transfer.

• Assist TSD with data products by providing highly skilled and authoritative expertise on data engineering methods and best practices, including code-first development approaches and modern pipeline design patterns. • Design, implement, and maintain an efficient, secure, stable, and flexible data architecture that supports products and end-users, with all assets managed via source control. • Design, implement, and maintain ELT/ETL pipelines for efficient processing of source data in Azure Synapse and Azure Machine Learning (using SDK V1 and SDK V2) • Review, maintain, and improve existing architecture and pipelines, including periodic audits to address bottlenecks, deprecated dependencies, and architecture drift. • Establish quality controls for maintaining all pipelines, and introduce error handling, logging mechanisms, and validation checks. • Incorporate source control for all pipelines and data analytics codebases to enable iterative code development while ensuring data architecture stability. • Optimize the ingestion, processing, and storage of a wide variety of datasets and data types, including modern columnar formats such as Parquet. • Develop self-service capabilities for SBA OIG analysts to query and export data for investigations and audits. • Coordinate with data scientists to ensure the architecture efficiently supports machine learning algorithms and data pipelines in Azure Machine Learning. • Develop robust standard operating protocols (SOPs) dictating the authoring, development, validation, publishing, execution, and monitoring of all data pipelines and assets in Azure environment. • Provide detailed documentation of the data architecture, including data dictionaries, ER diagrams, and pipeline process maps. • Maintain and expand the environment with additional datasets and services upon request, following a defined intake and testing process prior to production deployment. • Stay current with emerging AI tools relevant to data engineering and contribute to exploratory efforts evaluating automation and LLM-assisted capabilities.

• Assist TSD with data analytic products by providing highly skilled and authoritative expertise on analytic methods. • Perform data quality analysis on source tables to identify any abnormalities or inconsistencies. • Review, maintain, and provide support using all existing fraud indicators developed by TSD. • Develop and refine repeatable processes for efficiently combining and analyzing large data sources (including relational tables, structured and unstructured datasets). • Collaborate closely with criminal investigators to determine and execute strategies to support cases; proactively address and communicate potential data quality issues and adapt analysis to shifting case needs; adhere closely to federal rules of criminal procedure governing protected information (e.g., Rule 6(e)). • Design, develop, and implement advanced statistical and machine learning models targeted at identifying fraud, improper payments, or non-compliance within SBA programs. • Utilize indicators of potential fraud, improper payments, or non-compliance to develop and test predictive models. • Assist in developing case leads for SBA OIG investigations based on model outcomes. • Coordinate with the Data Engineering Team to ensure system architecture is designed to efficiently support machine learning models. • Integrate and scale natural language processing methods to parse, clean, and analyze large corpora of unstructured or semi-structured text data, utilizing OCR, semantic similarity algorithms, and large language models as needed. • Provide detailed documentation of all methodology, test models, and production models; maintain records in compliance with criminal evidentiary requirements. • Develop visualizations and dashboards to demonstrate methodological choices, outcomes, and predictive capabilities of machine learning models; iteratively solicit and integrate end-user feedback. • Provide outcomes and recommendations in multiple formats, including data summaries and visualizations for investigative staff, and executive summaries for SBA OIG leadership. • Create programming and automation techniques to improve task efficiency using tools such as SharePoint, Python, Microsoft Excel, Power BI, and others. • Identify new business questions to expand the scope of analysis and reporting.

• Initiate outbound calls, emails, and messages to potential clients. • Respond promptly to inbound inquiries and guide clients through initial questions. • Engage in conversations to uncover client needs and goals. • Clearly explain service offerings and recommend next steps. • Schedule follow-up calls or connect clients with the right team members. • Log interactions and maintain accurate notes in our CRM.

• Initiate outbound calls, emails, and messages to potential clients. • Respond promptly to inbound inquiries and guide clients through initial questions. • Engage in conversations to uncover client needs and goals. • Clearly explain service offerings and recommend next steps. • Schedule follow-up calls or connect clients with the right team members. • Log interactions and maintain accurate notes in our CRM.

• Initiate conversations with potential clients and respond to inbound inquiries. • Understand each client’s unique goals, challenges, and decision-making process. • Recommend tailored financial solutions based on client input and available data. • Guide clients through onboarding, product selection, and service implementation. • Maintain regular contact with clients to support retention and satisfaction. • Communicate insights and client feedback to internal teams to help improve offerings.

• Identify potential clients and assess their goals, challenges, and needs. • Build lasting partnerships with decision-makers and key stakeholders. • Stay current on trends, tools, and developments that may impact client strategies. • Communicate with clients via phone, email, video calls, and other professional platforms. • Recommend personalized financial solutions and services based on client needs. • Act as a trusted resource by providing a seamless onboarding process and ongoing support. • Collaborate with internal teams to continuously improve client experience.

• Engage with new and prospective clients through outreach and follow-ups. • Understand each client’s financial goals and decision-making process. • Recommend solutions that align with their needs. • Guide clients through onboarding and product implementation. • Maintain accurate records in CRM tools. • Support clients with regular check-ins and ongoing service.

• Initiate outbound calls and respond to inbound client inquiries. • Conduct needs assessments to understand personal or business objectives. • Recommend appropriate services and walk clients through the onboarding process. • Manage follow-up outreach and relationship-building activities. • Maintain accurate CRM records and activity logs.