Freelance Semantic and Genie Engineer

India

Remote | Long-Term | High-Impact Enterprise Products

We are hiring experienced Semantic and Genie Engineer for a long-term remote engagement with globally distributed engineering teams building scalable fintech and enterprise-grade platforms.

This role is for you if

You are currently employed full-time and open to a part-time freelance engagement on the side
You work 100% remotely in your primary role , no office setup required here either
You want stable, meaningful additional income without switching jobs
You are comfortable with a freelance/contract model, no full-time conversion on offer

About CodeVyasa

We are a product engineering firm partnering with top-tier global organizations across fintech, consulting, retail, and consumer tech. Our 750+ engineering team delivers high-impact solutions in Product Engineering, Data Engineering, Agentic AI, and GenAI. You will work alongside senior engineers on enterprise-scale products that ship to millions of users.

Key Responsibilities

Perform data discovery, profiling, source-to-business mapping, and analytical requirements translation.

Design and optimize SQL datasets, dimensional/semantic models, views, and materialized views with clear grain and aggregation rules.

Build and maintain governed Databricks Metric Views, including measures, dimensions, relationships, and business definitions.

Configure and improve Genie Agents/Spaces with instructions, sample questions, trusted SQL patterns, and benchmark questions.

Test answer accuracy, business semantics, performance, and consistency; diagnose fan-out, double counting, and incorrect responses.

Implement and evaluate cross-domain Genie Agent routing/orchestration and integration patterns as required.

Partner with business stakeholders, data product teams, and Solution Architecture to deliver reusable, production- Ready solutions.

Required Technical Skills

SQL, Data Discovery & Modeling

Advanced SQL: joins, CTEs, window functions, aggregation, deduplication, date logic, and query optimization.

Data profiling: grain, cardinality, nulls, freshness, relationships, historical/SCD behavior, and data quality.

Dimensional and semantic modeling: facts, dimensions, conformed dimensions, KPI definitions, aggregation correctness, and fan-out prevention.

Databricks Semantic Layer

Hands-on Databricks SQL, Unity Catalog, Delta Lake, views, and materialized views.

Engagement Model

Commitment: ~35 Hours/Week
100% Remote & Flexible Work Environment
Long-Term Engagement Stability
Consistent Monthly Payouts with Transparent Payment Cycles
No Hidden Deductions or Payroll Complexities

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