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.

- Robust 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.

📌 Data Architect – AWS & Snowflake (Bengaluru)
🏢 Synectics Apac
📍 Bengaluru