ANALYTICS ENGINEERDEPARTMENT:BUSINESS INTELLIGENCE / DATAABOUT XTENDOPSXO delivers outsourced customer experience and business operations, built on operational excellence, AI-driven innovation, and a bold approach to problem-solving.We help companies run smarter, operate more efficiently, and drive measurable business impact.We are in the middle of a massive evolution, expanding beyond traditional outsourcing into a next-generation model that integrates AI, automation, and SaaS-driven solutions to deliver outcome-focused business services.Our vision is not just to improve CX.It is to reinvent how businesses leverage technology, data, and human expertise to create intelligent, scalable, and revenue-generating service models.MAIN JOB OBJECTIVE:The Analytics Engineer owns data modeling for a portfolio of client accounts and builds the shared metric models that every account depends on.This role consolidates core contact center metrics onto master dimensions and shared definitions so that onboarding a new client becomes largely a configuration exercise, while also building the data layer behind a client-facing analytics product.This is a hands-on modeling role, focused on production data models rather than infrastructure, orchestration, or CI configuration.RESPONSIBILITIES AND MAIN ACTIVITIESAccount Modeling and Delivery:- Own data modeling for a portfolio of small, medium, and large client accounts, from ingested source data through to serving-ready output.
- Onboard new accounts through the standard scaffolding, and improve that scaffolding along the way.
- Deliver and support the recurring reporting those accounts depend on, working with the analyst assigned to each account.
- Diagnose and fix data issues in owned models, including tracing a wrong number back through the transformation chain to its source.Shared Metric Foundation:- Build and maintain the core metric models shared across all accounts: handle time, CSAT, quality scores, first contact resolution, SLA attainment, and workforce measures such as login time, break time, and worked hours.
- Contribute to the master and conformed dimensions that let the same logic serve clients running different source systems,



including the mapping from client-specific source values to master definitions.
- Maintain the master metric dictionary alongside the analyst team: what each metric means, at what grain, with what exclusions, and a worked example.
- Propose the shared version where a requested metric already exists in shared form.Quality and Contracts:- Write tests as part of the modeling work, covering both structure and business logic.
- Apply model contracts and versioning on outputs that downstream consumers depend on, so a breaking change is caught in CI rather than in a client's report.
- Take part in freshness and anomaly monitoring for owned models, and in incident post-mortems.Working with Analysts:- Review modeling contributions from analysts and raise the standard of what comes through.
- Translate requirements that arrive as a spreadsheet and a conversation into a specification that can be built and tested.
- Ask the questions that prevent the wrong thing being built: what decision does this metric support, at what grain, and what should it exclude.Documentation and Sprint Delivery:- Write and maintain documentation and runbooks for owned accounts and models, to the standard that someone else can operate them.
- Work within the team's sprint cadence: sized stories, planning, review, retrospective, and a definition of done that includes tests and documentation.
- Contribute to architectural decision records when a modeling decision is worth writing down.QUALIFICATIONS AND EXPERIENCE- 3–5 years in analytics engineering, data engineering, or a BI role with substantial hands-on data modeling.
- Hands-on dbt experience required: models, sources, tests, refs, and a practical grasp of materializations and incremental strategies.
- Strong SQL, including window functions,



complex aggregation, and the ability to reason about query performance and cost.
- Dimensional modeling fundamentals: facts, dimensions, and grain.
- Testing discipline, with the ability to explain what a test is protecting against.
- Git and pull-request workflow, comfortable giving and receiving review on SQL.
- Working data literacy beyond own layer: ability to read a pipeline dependency graph and understand what depends on what.
- Enough Python to read and modify existing ingestion code.
- Comfort with ambiguity and incomplete requirements, with the judgment to know when to ask and when to propose.
- Clear written communication in English.PREFERRED- Contact center, BPO, or workforce management metrics: handle time, CSAT, QA, FCR, SLA, adherence, occupancy, and agent-level WFM measures.
- Slowly changing dimensions, particularly for employee or roster data where role changes, transfers, and terminations have to be reconstructable as of a past date.
- Ingestion from imperfect sources such as spreadsheets, shared drives, and manual trackers.
- Snowflake specifically, including an awareness of what drives warehouse cost.
- Multi-client or multi-tenant modeling, where one set of logic serves clients with different source systems and different definitions of the same metric.
- Reporting that carries financial consequence, such as compensation, incentive, or billing calculations.
- Experience working in a layered or medallion-style warehouse architecture.
- Experience supporting analysts or business users as consumers of models, rather than only other engineers.AVAILABILITY- Full-time, remote, with overlap required across North America and Asia-Pacific business hours.This is a full-time contractor role.SKILLS- Data Modeling and Dimensional Design- dbt (Models, Tests, Contracts, Versioning)- Advanced SQL- Git and Pull-Request Review- Python (Reading and Modifying Ingestion Code)- Analytical and Strategic Thinking- Root Cause Analysis- Documentation and Runbook Writing- Stakeholder and Analyst Communication- Agile/Scrum Sprint Delivery- Adaptability

📌 Performance Management Engineer (Xico)
🏢 XtendOps
📍 Xico