Role: Lead Data Engineer And Architect
Relevant Experience: At least 8+ years of relevant experience are required
Location: Bengaluru, KA and Coimbatore
Notice period: We are Open – Preferred Who can Join as soon as possible
Opportunity: Full time
About Aezion:
Aezion is a technology solutions provider specializing in custom software, AI-driven solutions, and enterprise digital transformation.
Aezion is one of the trusted digital engineering providers in the USA and we live by the adage that our word is our bond. Our Promise is to get it right or make it right. We accomplish this by investing the effort to exceed client expectations from start to finish – architecting, designing, developing, hosting, maintaining, and supporting our clients throughout the project lifecycle. We believe that work is ministry – an expression of our values. Our goal is to honor our commitments to clients and the life energies of Aezion employees through results that transform clients into lifelong partners.
Working at Aezion:
Aezion is a mission-driven growing company fueled by our Purpose (Love others like Christ) and guided by our values (Love, Dependability, Humble, Diversity, Speed and Innovation). Our Purpose is why we exist. Our Values drive how we go about that existence and represent who we are. Service defines us at Aezion. Our 200+ dedicated, aligned employees pour their life energies to transform our customers into lifelong partners through service excellence.
Role Summary:
We are seeking a Senior Lead Analytics Engineer with deep expertise in Snowflake and dbt to design, build, and govern modern data warehouse frameworks that enable trusted AI, advanced analytics, and BI use cases.
This role goes beyond building pipelines—you will define the architectural patterns, data frameworks, and best practices that turn raw data into a scalable, reusable, and business aligned Unified Data Model (UDM) and semantic layer. The ideal candidate has a strong data modeling mindset and understands how architecture decisions directly impact AI readiness, analytics accuracy, and business trust.
Key Responsibilities:
Data Architecture & Framework Design:
- Design and build enterprise-grade data warehouse architectures on Snowflake.
- Define data frameworks and standards covering:
- Layered modeling (raw, staging, curated, semantic)
- Naming conventions and modeling patterns
- Reusable transformation and metric frameworks
- Establish best practices for scalability, performance, governance, and cost efficiency.
- Ensure architecture supports AI, BI, and self-service analytics consistently.
Data Warehouse, UDM & Semantic Layer:
- Data Warehouse, UDM & Semantic Layer
- Design and maintain Unified Data Models (UDM) aligned to business domains.
- Build robust semantic layers enabling reusable metrics and dimensions.
- Standardize business definitions to support:
- BI reporting
- Advanced analytics
- AI / ML feature consumption
- Ensure models are understandable, extensible, and well-documented.
dbt & Analytics Engineering:
- Lead dbt-based transformation frameworks across the platform.
- Implement and govern dbt best practices:
- Modular model design
- Testing, snapshots, and documentation
- Data lineage and exposures
- Embed data quality, freshness, and validation into the transformation layer.
- Integrate dbt workflows into CI/CD pipelines for repeatable deployments.
AI & BI Enablement:
- Design curated datasets optimized for AI and advanced analytics use cases.
- Enable BI tools (Power BI, Tableau, Looker, etc.) with performant, analytics-ready models.
- Ensure datasets are explainable, traceable, and consistent across consumption layers.
- Collaborate with AI, analytics, and business teams to align data design with use cases.
Leadership & Governance:
- Act as a technical lead and architect for analytics engineering initiatives.
- Mentor engineers and enforce modeling, testing, and documentation standards.
- Drive adoption of data modeling, metric, and semantic best practices enterprise-wide.
- Translate business requirements into scalable, governed data solutions.
Required Qualifications:
- 8+ years of experience in analytics engineering or data engineering.
- Strong hands on experience with Snowflake and dbt.
- Deep expertise in dbt and modern analytics engineering practices.
- Proven experience designing data warehouses, UDMs, and semantic layers.
- Excellent SQL skills with a strong data modeling foundation.
- Experience building architectural frameworks and best practices, not just pipelines.
Preferred Qualifications:
- Strong design and build experience in modern data architecture patterns
- Experience with BI tools and metric/semantic modeling frameworks.
- Experience enabling AI / ML workloads using curated data models
- Excellent communication and data storytelling skills.