Data Architect – AWS & Snowflake

Key Responsibilities
Data Architecture & Engineering
Design and implement scalable, secure, and high-performance cloud data architectures on AWS.
Define data architecture standards, patterns, frameworks, and best practices across the organization.
Design and develop robust data pipelines and ETL/ELT workflows for batch and near-real-time data processing.
Build and optimize enterprise data warehouses and analytical data platforms using Snowflake.
Design data integration solutions across multiple internal and external data sources.
Implement Change Data Capture (CDC) and incremental data processing patterns.
Ensure data platforms are scalable, reliable, maintainable, and cost-efficient.
Data Modelling & Warehousing
Design conceptual, logical, and physical data models.
Develop dimensional models using Kimball methodology, including fact and dimension tables.
Define data marts and analytical models supporting business reporting and analytics.
Design and maintain data structures that support both operational and analytical workloads.
Establish data modelling standards and governance practices.
Data Engineering & Orchestration
Develop and maintain production-grade pipelines using Python and PySpark.
Build and manage workflow orchestration using Apache Airflow.
Develop transformation frameworks using dbt.
Optimize SQL queries, Snowflake workloads, data processing jobs, and pipeline performance.
Implement monitoring, logging, alerting, error handling, and data quality checks across pipelines.
Semantic & BI Layer
Design and maintain the semantic/data consumption layer for BI and analytics.
Work closely with BI and business teams to create trusted, reusable datasets and metrics.
Ensure consistent definitions and business logic across reporting and analytical use cases.
Support self-service analytics by providing well-structured and governed data products.
AI/ML & Modern Data Products
Partner with AI/ML teams to design data foundations for machine learning, GenAI, and analytical products.
Support data pipelines and architectures for LLM/RAG-based applications and AI data products.
Ensure data platforms can support high-volume data processing and evolving AI/ML workloads.
Contribute to data architecture patterns for model training, feature generation, retrieval, and inference workflows.
AI-Assisted Engineering
Use modern AI-powered development tools such as GitHub Copilot, Cursor, Claude Code, or equivalent to improve engineering productivity.
Apply AI-assisted development responsibly for coding, testing, debugging, documentation, refactoring, and technical analysis.
Identify opportunities to incorporate AI into engineering workflows and accelerate delivery without compromising quality or security.
Collaboration & Technical Leadership
Collaborate with Data Engineering, Data Science, BI, Product, and business stakeholders.
Translate business requirements into scalable technical data solutions.
Provide technical leadership and mentorship to data engineers and other technical team members.
Conduct architecture reviews and establish engineering best practices.
Troubleshoot complex data architecture and pipeline issues and drive them through to resolution.
Required Skills & Experience
12-17 years of overall experience in Data Engineering, Data Architecture, or related fields.
Strong hands-on expertise in AWS data services and cloud architecture.
Expert-level experience with Snowflake, including architecture, performance optimization, data modelling, and warehouse design.
Expert-level SQL skills.
Strong programming experience with Python.
Strong experience with PySpark and distributed data processing.
Hands-on experience with Apache Airflow for workflow orchestration.
Hands-on experience with dbt for data transformation and modelling.
Strong experience in ETL/ELT, data integration, CDC, and pipeline development.
Strong knowledge of modern data warehousing and dimensional modelling.
Hands-on experience with Kimball dimensional modelling methodology.
Experience designing semantic/BI layers and analytical data models.
Strong understanding of data quality, governance, security, monitoring, and performance optimization.
Experience designing scalable data platforms supporting large volumes of data.
Hands-on experience with AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, or similar.

Work arrangement
No