Cloud Data Engineer

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, or related fields
• 5+ years in enterprise cloud data environment support, automation, and pipeline management
• Certifications such as AWS Data Analytics, Solutions Architect, or equivalent are advantageous
• Strong coding, troubleshooting, and optimization skills
• Excellent collaboration, communication, and stakeholder engagement skills
• Experience working within large-scale data ecosystems supporting business analytics
• Professional Competencies
• Critical thinking and analytical problem-solving for high-volume data pipelines
• Leadership and team collaboration in cross-functional environments
• Effective communication for stakeholder alignment and documentation
• Adaptability to evolving cloud and data technologies
• Ownership of system reliability, security, and operational excellence
• Time management and planning skills to meet continuous delivery goals
• S YNECHRON'S DIVERSITY & INCLUSION STATEMENT
• Diversity & Inclusion are fundamental to our culture, and Synechron is proud to be an equal

Benefits

Additional Information

Job Summary Synechron is seeking a highly skilled Cloud Data Engineer to design, develop, and optimize scalable, cloud-based data pipelines supporting advanced analytics and data warehousing initiatives. In this role, you will leverage AWS cloud services, orchestrate data workflows with Airflow, build modular data transformation models using DBT, and manage Snowflake data warehouses. Your expertise will enable reliable, high-performance data architectures aligned with enterprise objectives, facilitating data-driven decision-making and operational efficiency across large-scale systems. Software Requirements Required: AWS services (S3, Lambda, Glue, EC2), Apache Airflow, DBT, Snowflake, SQL (MySQL, PostgreSQL, or Snowflake), Git, Jenkins, CI/CD automation tools (GitHub Actions, GitLab CI, Azure DevOps) Preferred: Kubernetes, Terraform, DataOps tools, monitoring/alerting platforms (CloudWatch, Prometheus, Grafana, Splunk) Experience level: 5+ years supporting enterprise cloud data pipelines, migration, and automation in large organizations Overall Responsibilities Design and implement scalable data pipelines utilizing AWS cloud services for analytics, reporting, and data management Develop and maintain workflows with Apache Airflow to automate data ingestion, transformation, and loading processes Build modular, testable data transformation logic using DBT, ensuring data quality and consistency across systems Manage and optimize Snowflake data warehouses for high-throughput analytical workloads Collaborate with cross-disciplinary teams to gather requirements, validate data models, and implement solutions Monitor pipeline health, troubleshoot issues, and optimize performance and reliability Automate deployment, scaling, and maintenance activities with Infrastructure as Code (Terraform, CloudFormation) Support migration projects, data integration, and cloud transitioning efforts supporting enterprise architecture Document architecture, data flow, and operational procedures for ongoing support and compliance Technical Skills (By Category) Programming Languages: Essential: SQL, Python, Shell scripting Preferred: Java, Scala (for big data processing) Data Management & Databases: Snowflake, PostgreSQL, MySQL; schema design, data validation, and performance tuning Cloud Technologies: AWS (preferred), Azure (preferred), basic support for GCP supported Frameworks & Libraries: DBT, Apache Airflow, Spark, Presto, Kafka (preferred for streaming) Development Tools & Methodologies: Terraform, CloudFormation, Git, Jenkins, CI/CD pipelines, Agile/Scrum, DataOps best practices Security & Compliance: Data encryption, IAM policies, audit logging, compliance with data standards (GDPR, HIPAA) Experience Requirements 5+ years supporting enterprise big data or cloud data infrastructure Proven experience designing and operating scalable data pipelines on cloud platforms Hands-on expertise with AWS, Snowflake, Airflow, and DBT for ETL and data management Experience supporting data migration, automation, and performance tuning in large systems Industry experience in finance, healthcare, retail, or enterprise analytics is preferred; relevant large-scale data support experience in other sectors also acceptable Day-to-Day Activities Develop, support, and optimize cloud data pipelines for enterprise analytics platforms Automate data workflows, deployment pipelines, and infrastructure provisioning Troubleshoot pipeline failures, data quality issues, and operational bottlenecks Collaborate with data scientists, analysts, and enterprise teams on data ingestion, transformation, and integration projects Support migration, cloud adoption, and system upgrades in line with enterprise architecture strategies Monitor and tune system performance, capacity, and security standards Maintain documentation for architecture, data models, and operational procedures

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