Data & Integration Analyst

Scalis LLC; Pune District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Scalis LLC seeks a data-focused professional to unify diverse data sources into a single operational data home. You will design the foundational data layer for AI-readiness, govern the canonical data model, and coordinate with engineering to maintain an adaptable integration layer.

This role emphasizes data quality, governance, and actionable dashboards for future ERP initiatives. Responsibilities include ingest validation, read-paths for AI agents, and leading migrations while applying Lean and

Qualifications

• 4+ years hands-on data and analytics experience with data-modeling
• Ability to design canonical data models across multiple source systems and articulate trade-offs in identity resolution and deduplication
• Deep knowledge of PostgreSQL including schema design, migrations, and query tuning
• SQL proficiency for data exploration, auditing, reconciliation, and cross-database validation
• Python expertise for building data transformation scripts and pipelines
• Experience building integrations with commercial SaaS APIs and handling schema changes
• Knowledge of data-layer security and role-based access control
• Experience applying AI tools to accelerate pipeline creation and data quality validation
• Ability to inspect, validate, and trust business metrics

Responsibilities

• Define and own the canonical data model for core entities across systems
• Set up upfront data validation rules to prevent bad records entering systems
• Coordinate with full‑stack engineers to maintain a flexible integration layer
• Build the semantic layer for Power BI and future ERP dashboards
• Create clean, permissioned read paths for AI agents to access data
• Own data quality and address recurring defects and data issues
• Lead data migrations from legacy systems preserving history and audit trails
• Apply Lean and AI methods to reduce data failure points and improve KPIs/SLAs
• Monitor unstructured data insights from speech, text, and sentiment data

Skills

Data modeling

SQL

Python

Data integration

Data quality

Data governance

Tools

PostgreSQL

dbt

Power BI

Tableau

Job description

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.