Data & Integration Analyst

SCALIS; Región Centro; Presencial; MXN 600.000 - 900.000; Jornada completa

Descripción de la vacante

SCALIS is building a unified data platform and seeks a data enthusiast to lead data modeling, ingests, and integration across accounts, contacts, candidates, deals, and work records. You will architect a canonical data model and a scalable data layer that enables AI agents to read data cleanly, with governance and future ERP readiness.

You will partner with full-stack engineers to define a robust ingestion validation, secure data access, and dashboards foundation via Power BI, aligning data

Formación

• 4+ years of hands-on data and analytics experience.

Responsabilidades

• Define entity structures, relationships, and identity resolution across accounts, contacts, candidates, deals, projects, and work records.
• Establish upfront validation rules at ingest to prevent low-quality records from entering systems.
• Coordinate integration layer with full-stack engineers to maintain adaptable integration layer.
• Build and govern the modeled semantic layer beneath Power BI transition for future ERP.
• Architect clean read paths so internal AI agents consume structured data directly.
• Own recurring systemic data defects and data quality improvements.
• Lead data migration paths off retired legacy systems with audit trails.
• Apply Lean principles and AI tools to reduce data failure points and KPIs/SLAs.
• Monitor unstructured data inputs like speech, text, and sentiment analyses.

Conocimientos

Data modeling

SQL proficiency

Python

Data quality

AI tooling

Data security

Herramientas

PostgreSQL

dbt

Tableau

Power BI

API integrations

Descripción del empleo

We need a data enthusiast who pairs sharp analytical capabilities with the leverage of modern AI tools. As our organization harmonizes multiple disparate systems, data sources, and analytics platforms into a single operational home, this role will lead and orchestrate the collection, cohesion, and maintenance of our core data models (unified accounts, contacts, candidates, deals, activity, and work records).

You will design and oversee the foundational data layer that allows future AI agents to read seamlessly across all entity data without an integration tax, setting the groundwork for our incoming ERP consolidation.

What You Own

• Canonical Data Model: Define entity structures, relationships, and identity-resolution logic across accounts, contacts, candidates, deals, projects, and work records.
• Validation at Ingest: Establish upfront validation rules that prevent low-quality records from entering systems, shifting focus from post-hoc error reporting to preventative data hygiene.
• Integration Layer Coordination: Collaborate with full-stack engineers to maintain an adaptable integration layer, ensuring vendor replacements require simple configuration rather than a full code rewrite.
• Reporting & Dashboards Foundation: Build and govern the modeled semantic layer beneath our Power BI transition, unifying legacy reports and setting up actionable dashboards for the future ERP.
• Agent Data Access: Architect clean, permissioned, and documented read paths so internal AI agents consume structured data directly rather than scraping front-end source systems.
• Data Quality Ownership: Serve as the direct point of contact and owner for recurring systemic data defects.
• Migration Strategy: Lead data migration paths off retired legacy systems while preserving historical continuity and audit trails.
• Lean AI & Continuous Improvement: Apply Lean principles and AI tools to reduce data failure points, detect early warning indicators, and define KPIs/SLAs to optimize organizational performance.
• Unstructured Data Insights: Monitor, analyze, and process speech, text, and sentiment analysis inputs across business channels.

Requirements

• 4+ years of hands-on experience in data and analytics, featuring heavy data-modeling responsibilities.
• Proven track record of designing a canonical data model across multiple source systems, with the ability to articulate trade-offs in identity resolution and deduplication.
• Deep knowledge of PostgreSQL (schema design, normalization trade-offs, database migrations, query performance tuning).
• SQL proficiency for data exploration, auditing, reconciliation, and cross-database validation.
• Python expertise for developing transformation scripts and data pipelines.
• Demonstrated experience building and supporting integrations with commercial SaaS APIs (handling rate limits, unexpected schema updates, and partial failures).
• Strong understanding of data-layer security, designing models where authorization and field-level restrictions are enforced structurally.
• Proactive adoption of AI tools to accelerate pipeline creation, audit data quality, and enrich datasets.
• Inherent curiosity and rigor for inspecting, validating, and establishing trust in business metrics.

Strong Preferences

• Direct familiarity with core enterprise data models across ERP, HRIS, WFM, or CRM platforms (e.g., NetSuite, SAP, Workday, Salesforce, Genesys, Oracle, or Aspect).
• Experience using tools like dbt (or comparable transformation and automated testing frameworks).
• Hands-on experience working with Tableau, Power BI, or similar analytics stacks, specifically using AI to extract insight from structured datasets.
• Practical experience consolidating legacy commercial systems onto unified or in-house platforms.
• Exposure to recruitment, workforce, or operations data, including handling candidate PII and data retention requirements.
• Prior experience preparing database schemas or data warehouse layers specifically to feed LLMs, AI workflows, or autonomous agent routines.