Business Analytics Specialist

Own and evolve the Bronze / Silver / Gold data architecture for the Global Metrics ecosystem.

Define scalable architectural patterns for integrating new Customer Support platforms and data sources.

Design dimensional, relational, and analytical data models.

Define standards for data ingestion, transformation, storage, and consumption.

Design integration patterns across APIs, cloud storage, SaaS platforms, and enterprise data platforms.

Establish architecture principles for data quality, traceability, lineage, scalability, and maintainability.

Partner with Data Engineering teams to translate architectural designs into production-ready implementations.

Review technical solutions and ensure alignment with Global Metrics architectural standards.

Design scalable data pipelines using Databricks and dbt.

Work with large-scale datasets and high-volume data ingestion workloads.

Define transformation patterns and data-processing strategies for distributed environments.

Optimize data models and processing pipelines for performance, scalability, reliability, and cost.

Support ingestion strategies using platforms such as Fivetran, APIs, file-based integrations, and cloud object storage.

Define reusable patterns for batch and incremental data processing.

Ensure pipelines are designed with appropriate monitoring, recoverability, and data-quality controls.

Own the design and evolution of the analytics semantic layer.

Define scalable frameworks for KPIs, business metrics, dimensions, and analytical entities.

Translate business concepts into standardized analytical definitions and data models.

Design and govern the Gold analytical layer used by dashboards, analytics, AI, and reporting.

Define dimensional and OLAP models optimized for analytical consumption.

Establish reusable semantic models to reduce duplicated business logic across dashboards and datasets.

Partner with Business Analysts and stakeholders to ensure metrics accurately represent business processes and operational reality.

Define and maintain standards for KPI definitions, calculations, dimensions, and business rules.

Maintain and evolve the business glossary and data dictionary.

Establish governance processes to prevent conflicting or duplicated metric definitions.

Define global naming conventions, taxonomies, tags, classifications, and analytical standards across Customer Support platforms.

Ensure business definitions are consistently translated into technical implementations.

Support the definition of global analytical entities such as customers, contacts, interactions, tickets, agents, queues, channels, and support journeys.

Define how new systems and vendors are integrated into the Global Metrics data ecosystem.

Evaluate integration alternatives such as APIs, event streams, file exports, object storage, managed ingestion platforms, and direct database connections.

Define source-of-truth strategies and relationships across multiple operational systems.

Design data models that enable end-to-end traceability across Customer Support interactions and platforms.

Participate in architecture discussions with internal Data, Engineering, Security, and Platform teams.

Work with vendors and technical stakeholders to assess data availability, integration capabilities, and architectural constraints.

AI & Advanced Analytics Architecture — Preferred

Bachelor’s degree in Computer Science, Information Systems, Data Engineering, Software Engineering, Statistics, or a related technical field.

Equivalent professional experience may be considered in place of formal education.

Advanced SQL

Dimensional data modeling

Data warehouse / lakehouse architectures

Medallion Architecture (Bronze / Silver / Gold)

Semantic layers and analytical data models

Data integration patterns

Experience working with large-scale datasets and high-volume data ingestion is required.

Experience designing and optimizing scalable data pipelines.

Experience with Fivetran or equivalent managed ingestion platforms is preferred.

Experience with Spark and distributed data processing is strongly preferred.

Experience designing analytics solutions consumed by BI platforms such as Hex, Tableau, Power BI, Looker, Mode, or equivalent tools.

LLM-based analytics

Context engineering

NLP / text analytics

AI data pipelines

Data-Centric AI principles