• Proactively monitors adherence to service level agreements (SLAs) using ServiceNow dashboards and performance reports, escalating deviations to program leadership. • Drives continuous service improvement by conducting in-depth analysis of service desk performance metrics and delivering data-driven corrective action recommendations. • Performs high-level systems analysis, evaluation, design, integration, documentation, and implementation of very complex applications requiring thorough knowledge of administrative and technical skills. • Participates in all phases of system development with emphasis on planning, analysis, evaluation, integration, testing, and acceptance phases (IV&V and DT&E). • Applies higher-level business or technical principles and methods to very difficult technical problems to arrive at automated engineering solutions. • Designs, analyzes, and maintains highly complex enterprise infrastructure environments to support J6 service delivery operations. • Develops performance measurements including goals, metrics, and reports utilizing the current service desk platform; facilitates a feedback system to team members on customer service, communication, and technical skills to enhance the quality of support delivered. • Develops and provides regular Service Desk metrics reports to program leadership and Government stakeholders. • Manages the implementation of an enterprise knowledge base and knowledge management best practices to ensure consistent, high-quality service delivery. • Collaborates with IT support teams and section leads to design and implement continuous service improvement initiatives aligned with J6 mission objectives. • Ensures J6 customer requirements for Service Desk performance are met consistently and in alignment with contractual obligations. • Analyzes tickets, calls, emails, and web chat interactions to provide Interaction and Ticket Trend Reports, characterizing the nature and volume of change in call and ticket volume and handling time. • Generates ad hoc and repeatable Interaction and Ticket Forecast Reports using supporting tools including Active Directory accounts, Asset Reports, and Mobile Device Usage Reports. • Provides business and data visualization dashboards in graphical format to inform Government leadership regarding patterns, trends, and correlations, enabling data-driven decision-making. • Forecasts future interaction volumes, handle times, staffing requirements, and schedules to ensure adequate J6 Service Desk coverage and performance.

• Build and maintain scalable ETL/ELT pipelines to ingest structured and unstructured data into the data warehouse. • Develop and optimize data models to support reporting, dashboards, and operational insights. • Write clean, efficient, and reusable SQL and Python code to transform raw data into business-ready outputs. • Collaborate with analytics and business stakeholders to understand data requirements and deliver practical solutions. • Monitor pipeline performance and ensure reliability, accuracy, and timeliness of data flows. • Design frameworks for triggering AI workflows based on business rules and data events. • Serve as a subject matter expert on data best practices, including schema design, data governance, and data security. • Partner with cross-functional teams to align data assets with enterprise goals, AI enablement, and automation strategies. • Drive adoption of new data tools and methodologies, such as DBT, Iceberg, or similar modern data stack technologies. • All other duties as assigned.

Role Description Analytics Engineering plays a crucial role in building robust and reliable data pipelines and data models that enable data-driven decision-making, powering various analytics, AI, and machine learning initiatives within Coursera. In addition, Analytics Engineering today owns many external facing data products that drive revenue and boost partner and learner satisfaction. As a Staff Analytics Engineer, you will be architecting high quality and scalable data pipelines powering business critical applications, leading enterprise wide data modeling strategies and driving a culture of transparent, well-governed data systems. You will collaborate with both technical and cross-functional leaders to lead and set direction on how we craft and look at data, while driving industry accepted standards on data governance, discoverability, and accessibility. You will craft technical decisions and design trade-offs to ensure we can speedily deliver on ambitious, innovative goals while building the foundations for extension and scale in years to come. Responsibilities - Architect scalable data models and construct high quality ELT pipelines that act as the backbone of our core data lake, with cutting edge technologies such as Airflow, DBT, Databricks, and Sigma. - Design, build, and launch self-serve analytics products from data consumption to data discovery and enablement. - Be a technical leader for the team, inspiring others with your proficiency in technical and architectural designs for major team initiatives. - Be a data leader for the business, increasing data literacy and resolving data gaps. - Partner with data scientists, business stakeholders, and product engineers to define, curate, and govern high-fidelity data. - Develop new tools and frameworks in collaboration with other engineers to enhance data access and efficiency. Qualifications - 10+ years experience in data/analytics engineering with expertise in data architecture, pipelines, and reporting. - Expert experience with relational databases, DRY data modeling practices, and efficient SQL code generation. - Expert experience with some of: AWS, Databricks, Delta Lake, Airflow, dbt, Redshift, Datahub; Databricks and dbt preferred. - Expert experience with crafting and driving self service reporting solutions with hands on experience in BI Tools; Looker or Sigma preferred. - Strong experience implementing Data Observability frameworks (e.g., Monte Carlo, Great Expectations) at an enterprise level. - Strong hands on experience with AI tools such as Claude, Gemini, Cursor and its role in streamlining data processing and enabling data democratization. - Strong experience with data lake architecture and batch and streaming architectures. - Strong experience in driving industry standards in data governance and technical best practices. - Strong ability to communicate technical concepts clearly and concisely to leadership. - Proven relationships with business end users with clear understanding of how data is used to power business decisions. - Independence and passion for innovation and learning new technologies. Preferred Qualifications - Strong Experience leading cross-functional RFCs (Request for Comments) and driving technical standards across multiple engineering pods or business disciplines. - Strong experience with data lake architecture and batch and streaming architectures. - Proven track record building feature stores or data pipelines specifically for LLM fine-tuning and RAG architectures. - Strong track record of leadership and mentorship in elevating data culture, preferably in a remote environment. Compensation / Location At Coursera, we offer competitive, zone-based pay aligned to your location, experience, and role level. Our total rewards package goes beyond salary, with comprehensive health and wellness benefits, bonus and RSU equity programs, and global perks designed to help you grow and thrive wherever you are. - This role is only available for hire in [insert location] - US Zone 1: $188,000 - $235,000 - US Zone 2: $182,000 - $228,000 - US Zone 3: $167,000 - $209,000 - US Zone 4: $156,000 - $195,000

• Build the Data Foundation • Design and operate the end-to-end gameplay analytics ecosystem. • Define tracking, storage, processing, and reporting architecture. • Own data correctness and make judgement calls when quality checks flag anomalies. • Drive AI-Native Analytics • Build the semantic layer that translates raw gameplay data into validated gameplay and business concepts. • Develop AI-driven automated reporting, dashboards, and insight-generation workflows. • Evaluate and implement AI tools, agents, and workflows that improve analytics speed, reliability, and depth. • Review AI-generated code and analytics on critical paths before they reach production. • Generate Product Intelligence • Identify opportunities, risks, anomalies, and trends across gameplay and player behavior. • Support product, balancing, design, and leadership teams with data-driven recommendations. • Translate complex datasets into clear, actionable insights. • Set the Agentic Bar • Help define how the wider engineering organization adopts agentic practices. • Establish standards for tooling, review processes, quality gates, and what "done" means when agents generate most of the implementation.