Cloud Data Engineer - Snowflake, DBT, Airflow and AWS

Job Summary

Synechron is seeking a Senior Cloud Data Engineer with strong expertise in Snowflake, DBT, Apache Airflow, and AWS to design, develop, and support scalable cloud data platforms.

The role combines hands-on data engineering, cloud platform implementation, infrastructure automation, solution design, architecture discussions, and stakeholder engagement. The successful candidate will deliver reliable, secure, high-performing, and maintainable data solutions that support business requirements and enterprise data initiatives.

This role contributes to business objectives by improving data platform scalability, automation, data quality, operational efficiency, governance, and the availability of trusted data for analytics and decision-making.

Software Requirements

Required Software Skills

• Snowflake: Minimum 4–6 years of hands-on experience designing, developing, optimizing, and supporting cloud data warehouse solutions.
• DBT: Practical experience developing models, tests, documentation, macros, sources, and reusable ELT workflows.
• Apache Airflow: Experience designing, scheduling, monitoring, and troubleshooting data workflows and dependencies.
• AWS Cloud Services: Minimum 4–6 years of hands-on experience implementing and supporting cloud data platforms and services.
• Python: Experience with data engineering, automation, orchestration, testing, and cloud platform support.
• SQL: Strong experience writing, optimizing, and troubleshooting queries for data transformation, validation, and analysis.
• Data warehousing and data modeling: Experience with dimensional modeling, data structures, data quality, data integration, and warehouse optimization.
• GitHub/Git: Experience with source control, branching, merging, pull requests, code reviews, and collaborative development.
• Terraform and/or CloudFormation: Experience implementing infrastructure as code for cloud data platforms and supporting infrastructure changes.
• CI/CD pipelines: Experience with Buildkite, Azure DevOps, Jenkins, GitHub Actions, or comparable CI/CD tools.
• AWS IAM, security, and monitoring: Understanding of identity and access management, secure configuration, logging, alerting, and cloud-platform monitoring.
• Software versions: Experience working with the versions of Snowflake, DBT, Airflow, AWS services, Terraform or CloudFormation, Python, SQL, and CI/CD tools used by the assigned Synechron project.

Preferred Software Skills

• Experience with enterprise-scale cloud data platform implementation.
• Familiarity with AWS data, storage, compute, networking, monitoring, and serverless services.
• Experience with cloud-native architectures, data lake or lakehouse patterns, and modern data engineering practices.
• Familiarity with data governance, metadata management, data lineage, data cataloguing, and data quality tools.
• Experience with automated testing, deployment validation, observability, and platform reliability practices.
• Experience with Agile delivery tools and work-management platforms.
• Exposure to solution architecture, technical leadership, and architectural decision records.

Equivalent tools and technologies may be considered where they provide comparable capabilities.

Overall Responsibilities

• Design, develop, and support scalable cloud-based data platforms on AWS.
• Build, optimize, and maintain ELT/ETL pipelines using Snowflake, DBT, and Apache Airflow.
• Develop reliable, reusable, tested, and documented data models and transformation workflows.
• Participate in solution design, architecture discussions, technical decision-making, and design reviews.
• Implement and manage infrastructure as code using Terraform and/or CloudFormation.
• Develop, maintain, and improve CI/CD pipelines for data platform deployments.
• Collaborate with business stakeholders, architects, data teams, engineering teams, security teams, and operations teams to deliver data solutions.
• Translate business and technical requirements into scalable, secure, maintainable, and operationally supportable data-platform solutions.
• Ensure platform reliability, performance, security, data quality, availability, and operational excellence.
• Troubleshoot production issues, pipeline failures, data-quality concerns, performance bottlenecks, and infrastructure problems.
• Provide ongoing cloud platform support and participate in incident response, root-cause analysis, and remediation activities.
• Drive best practices around automation, data governance, monitoring, observability, documentation, and cloud optimization.
• Contribute to technical leadership by reviewing designs, guiding implementation decisions, and supporting knowledge sharing.
• Promote sustainable data engineering practices by optimizing compute and storage usage, reducing unnecessary data movement, reusing pipeline components, and supporting efficient cloud operations.
• Deliver solutions that meet agreed requirements for quality, security, scalability, performance, maintainability, and operational support.
• Measure outcomes through pipeline reliability, data quality, deployment efficiency, platform performance, cost efficiency, and stakeholder satisfaction.

Technical Skills (By Category)

Programming Languages

Essential

• Strong proficiency in Python for data engineering, automation, orchestration, validation, testing, and cloud platform support.
• Advanced SQL skills for data transformation, modeling, validation, optimization, and troubleshooting.
• Ability to write modular, reusable, maintainable, and testable code.
• Experience handling structured and semi-structured data in cloud data platforms.

Preferred

• Familiarity with additional programming or scripting languages used for cloud automation or data processing.
• Experience developing reusable Python libraries, utility functions, data-quality frameworks, or deployment automation.
• Exposure to software engineering practices such as unit testing, static analysis, and code-quality management.

Databases/Data Management

Essential

• Strong hands-on experience with Snowflake .
• Experience with data warehousing, data modeling, data integration, and data transformation.
• Understanding of dimensional modeling, fact and dimension structures, slowly changing dimensions, data partitioning, and query optimization.
• Experience developing DBT models, tests, sources, documentation, macros, and dependencies.
• Understanding of data quality, validation, reconciliation, lineage, metadata, and data availability.
• Ability to optimize warehouse workloads and manage data-processing efficiency.

Preferred

• Experience with data lakes, lakehouses, external tables, semi-structured data, or modern cloud data architectures.
• Familiarity with data cataloguing, governance frameworks, master data, reference data, and data-retention practices.
• Experience supporting analytics, reporting, operational data, or machine-learning data requirements.

Cloud Technologies

Essential

• Minimum 4–6 years of hands-on experience with AWS .
• Experience designing, deploying, and supporting cloud-based data platforms.
• Understanding of cloud compute, storage, networking, identity, monitoring, scalability, availability, and resilience.
• Experience applying AWS IAM and related security controls.
• Ability to assess cloud architecture options based on performance, reliability, security, maintainability, and cost.

Preferred

• Experience with AWS services supporting data ingestion, storage, processing, orchestration, monitoring, and serverless workloads.
• Familiarity with cloud-native architectures, landing zones, high availability, disaster recovery, and environment management.
• Experience optimizing cloud consumption and reducing unnecessary compute, storage, and data-transfer costs.

Frameworks and Libraries

Essential

• Strong experience with DBT for cloud data transformations and model management.
• Strong experience with Apache Airflow for workflow orchestration, scheduling, de