Integration Engineer

Sonata Software•Irving, TX

4d•Remote

About The Position

Sonata Software is seeking a Senior Integration Engineer to design and support enterprise-scale data platforms and integration solutions. The role focuses on data engineering, AWS cloud technologies, Python, SQL, Databricks, workflow orchestration, and modern DevOps practices. The engineer will independently analyze business requirements, identify gaps, develop robust data solutions, and build scalable data pipelines while collaborating with cross-functional teams. The position is described as a contract engagement and remote within the US. Responsibilities include developing and maintaining cloud-native data solutions and pipelines, supporting database migrations and Databricks initiatives, orchestrating workflows, implementing automated testing, and improving reliability, scalability, and operational efficiency. The role also includes CI/CD, containerized deployments, production troubleshooting, cloud resource optimization, and adherence to security and governance practices. Sonata Software is an Equal Opportunity Employer and encourages diversity in the workplace.

Requirements

Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
7–15 years of hands-on experience in Data Engineering, Cloud Engineering, or Integration Engineering roles.
Proven experience designing and supporting enterprise-scale data platforms and integration solutions.
Strong analytical, problem-solving, and troubleshooting skills.
Experience working in Agile development environments and collaborating with cross-functional teams.
Excellent communication and stakeholder management skills.
Experience with AWS cloud services, including S3, EC2, and IAM; Lambda and CloudWatch are preferred.
Experience with Docker and Kubernetes.
Proficiency in Python and advanced SQL.
Experience with data pipeline development and ETL/ELT processes.
Experience with Databricks and workflow orchestration using Apache Airflow and Astronomer.
Experience with database design and optimization, Alembic, and schema migration management.
Experience with CI/CD pipelines, Git/GitHub/GitLab, code quality, and release management.
Experience with Python-based test automation and AI-assisted testing frameworks such as Grok or equivalent AI-powered platforms.

Nice To Haves

PySpark and Spark optimization.
AWS Glue.
Terraform or other infrastructure-as-code tools.
Kafka or event-driven architectures.
Snowflake or modern data warehouses.
REST API development and integration.
Monitoring tools such as Datadog, Prometheus, and Grafana.
Machine learning and AI integration exposure.
Healthcare domain experience.
Experience with large language models (LLMs) and AI-enabled automation frameworks.
AWS Lambda and CloudWatch experience.

Responsibilities

Design, develop, and maintain scalable, reliable data ingestion, transformation, and integration pipelines using Python and SQL.
Build and manage cloud-native data solutions in AWS, including services such as S3, EC2, and IAM.
Develop, enhance, and maintain database migration frameworks using Alembic to support schema evolution with minimal downtime.
Orchestrate, monitor, and optimize data workflows using Apache Airflow and Astronomer.
Collaborate with product owners, data engineers, analysts, and business stakeholders to understand requirements and deliver production-grade solutions.
Lead and support migrations to Databricks to improve performance, scalability, and analytics capabilities.
Create automated validation and testing frameworks using Python and AI-assisted testing tools or frameworks.
Identify process gaps, recommend improvements, and independently design solutions to enhance system reliability and efficiency.
Implement containerized applications using Docker and deploy workloads on Kubernetes.
Drive CI/CD implementation, code quality standards, release management, and infrastructure automation.
Monitor system performance, troubleshoot production issues, and optimize cloud resources for cost and operational efficiency.
Ensure adherence to security, governance, and best practices for cloud and data engineering platforms.