Cloud Data Engineer - Snowflake, DBT, Airflow and AWS

• 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.

• Experience with enterprise-scale cloud data platform implementation.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• Strong experience with DBT for cloud data transformations and model management.
• Strong experience with Apache Airflow for workflow orchestration, scheduling, dependency management, monitoring, and recovery.
• Experience with Snowflake development and integration patterns.
• Experience designing reusable data pipeline components and configuration-driven workflows.
• Ability to integrate data workflows with cloud services, source control, CI/CD, monitoring, and security controls.

• Familiarity with DBT packages, reusable macros, incremental models, snapshots, and advanced testing patterns.
• Experience with Airflow operators, sensors, hooks, DAG design, task mapping, scheduling, and operational optimization.
• Exposure to data-quality, observability, metadata, lineage, or data-catalogue frameworks.

• Experience implementing infrastructure as code using Terraform and/or CloudFormation .
• Experience developing and supporting CI/CD pipelines using Buildkite, Azure DevOps, Jenkins, GitHub Actions, or comparable tools.
• Experience with Git or GitHub-based development workflows, including pull requests, branching, merging, tagging, and code reviews.
• Understanding of Agile development and iterative delivery practices.
• Experience with deployment automation, testing, release management, rollback planning, and environment promotion.
• Experience troubleshooting production issues and performing root-cause analysis.
• Ability to create and maintain technical documentation, runbooks, architecture diagrams, and operational procedures.

• Experience with automated infrastructure testing, deployment validation, policy checks, and release governance.
• Familiarity with monitoring, alerting, logging, observability, and platform-health dashboards.
• Experience with technical leadership, solutioning, architecture reviews, and decision documentation.
• Exposure to platform engineering, self-service deployment, and reusable cloud infrastructure modules.

• Strong understanding of AWS IAM, role-based access, least-privilege permissions, authentication, authorization, and secure service integration.
• Experience protecting sensitive data through appropriate access controls, encryption, masking, retention, and secure data-handling practices.
• Understanding of secure infrastructure-as-code, CI/CD, source control, secrets management, and deployment practices.
• Ability to identify and address security risks across data pipelines, cloud infrastructure, platform services, and operational workflows.
• Awareness of auditability, compliance, data privacy, and governance requirements.

• Experience with secrets-management tools, key management, encryption, network controls, private connectivity, and security monitoring.
• Familiarity with policy-as-code, vulnerability management, security testing, and cloud compliance controls.
• Experience implementing security requirements in regulated or highly governed environments.

• 8–10 years of overall IT experience in data engineering, cloud engineering, platform engineering, solution engineering, or a related field.
• Minimum 4–6 years of hands-on experience with Snowflake and AWS .
• Proven experience delivering cloud data engineering solutions end to end, from requirements and architecture through implementation, deployment, and operational support.
• Strong experience with Snowflake, DBT, Apache Airflow, Python, SQL, data warehousing, data modeling, and cloud data platforms.
• Experience implementing infrastructure as code using Terraform and/or CloudFormation.
• Experience developing CI/CD pipelines for data platform deployments.
• Experience with AWS IAM, security, monitoring, platform reliability, and cloud optimization.
• Experience participating in solutioning, architecture discussions, technical decision-making, and technical leadership.
• Experience working in Agile environments and collaborating with business stakeholders, architects, engineering teams, and technology teams.
• Financial services, banking, or collections dom