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.
- Git Hub/Git: Experience with source control, branching, merging, pull requests, code reviews, and collaborative development.
- Terraform and/or Cloud Formation: Experience implementing infrastructure as code for cloud data platforms and supporting infrastructure changes.
- CI/CD pipelines: Experience with Buildkite, Azure Dev Ops, Jenkins, Git Hub 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 Cloud Formation, 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 Cloud Formation.
- 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, 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.

Preferred

- 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.

Development Tools and Methodologies

Essential

- Experience implementing infrastructure as code using Terraform and/or Cloud Formation.
- Experience developing and supporting CI/CD pipelines using Buildkite, Azure Dev Ops, Jenkins, Git Hub Actions, or comparable tools.
- Experience with Git or Git Hub-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.

Preferred

- 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.

Security Protocols

Essential

- Solid 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.

Preferred





- 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.

Experience Requirements

- 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 Cloud Formation.
- 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 domain experience is desirable.
- Candidates may also qualify through equivalent combinations of cloud data engineering, data platform development, solution architecture, Dev Ops engineering, analytics engineering, or cloud platform implementation experience that demonstrate the required capabilities and outcomes.

Day-to-Day Activities

- Develop, optimize, monitor, and support Snowflake, DBT, and Airflow pipelines, data models, workflows, and cloud data platform components.
- Collaborate with business stakeholders, architects, engineering teams, data teams, and security teams to clarify requirements, review designs, and deliver data solutions.
- Automate infrastructure and deployments using Terraform or Cloud Formation, maintain CI/CD pipelines, investigate production issues, and improve platform reliability.
- Make technical decisions within the agreed architecture and governance framework, balancing performance, security, scalability, cost, and maintainability; escalate material risks when required.

Qualifications

- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Technology, Engineering, or a related field; equivalent relevant professional experience may be considered.
- Demonstrated experience delivering scalable cloud data platforms using Snowflake, AWS, DBT, Airflow, Python, SQL, and infrastructure-as-code practices is required.
- AWS, Snowflake, data engineering, Terraform, or related professional certification is preferred but not mandatory.
- Relevant training in cloud architecture, data warehousing, DBT, Airflow, infrastructure as code, CI/CD, security, data governance, or Agile delivery is preferred.
- Commitment to continuous professional development through technical learning, certification, knowledge sharing, architecture practice, and awareness of evolving cloud data engineering standards.

Skilled Competencies

- Apply critical thinking and structured problem-solving to diagnose pipeline failures, data-quality issues, platform incidents, performance constraints, and architectural risks.
- Provide technical leadership through design reviews, knowledge sharing, implementation guidance, and effective collaboration across data, engineering, architecture, security, and business teams.
- Communicate technical concepts, architecture options, delivery progress, risks, dependencies, and recommendations clearly to technical and non-technical stakeholders.
- Adapt to changing business requirements, cloud services, data-platform capabilities, security controls, delivery practices, and emerging data engineering technologies.
- Identify opportunities to improve automation, data quality, platform reliability, governance, cost efficiency, observability, scalability, and sustainable cloud usage.
- Manage multiple platform initiatives, production priorities, stakeholder expectations, dependencies, and delivery commitments while maintaining attention to detail and technical quality.

Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal opportunity workplace and is an affirmative action employer. Our Diversity, Equity, and Inclusion (DEI) initiative ‘Same Difference’ is committed to fostering an inclusive culture – promoting equality, diversity and an environment that is respectful to all. We strongly believe that a diverse workforce helps build stronger, successful businesses as a global company. We encourage applicants from across diverse backgrounds, race, ethnicities, religion, age, marital status, gender, sexual orientations, or disabilities to apply. We empower our global workforce by offering flexible workplace arrangements, mentoring, internal mobility, learning and development programs, and more.

All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant’s gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law.

📌 Cloud Data Engineer - Snowflake, DBT, Airflow and AWS (India)
🏢 Synechron
📍 India