Integration Engineer

1 day ago

Irving, TX, USASenior

Responsibilities

Design, develop, and maintain scalable data ingestion, transformation, ETL/ELT, and integration pipelines using Python and SQL.
Build and manage cloud-native data solutions within AWS, including S3, EC2, IAM, Lambda, and CloudWatch.
Develop and maintain Alembic database migration frameworks and manage schema evolution with minimal downtime.
Orchestrate, monitor, and optimize workflows using Apache Airflow and Astronomer.
Support Databricks migration initiatives and improve data platform performance, scalability, and analytics capabilities.
Create Python-based validation and testing frameworks using AI-assisted testing tools such as Grok or equivalent platforms.
Implement containerized applications with Docker and deploy workloads on Kubernetes.
Drive CI/CD implementation, code quality standards, release management, and infrastructure automation.
Monitor performance, troubleshoot production issues, optimize cloud resources, and support security and governance best practices.
Collaborate with product owners, data engineers, analysts, and business stakeholders to analyze requirements and deliver production-grade solutions.

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.
Experience designing and supporting enterprise-scale data platforms and integration solutions.
Strong expertise in AWS, Python, advanced SQL, Databricks, data pipelines, workflow orchestration, database migration, testing automation, and DevOps practices.
Experience with Docker, Kubernetes, Apache Airflow, Astronomer, Alembic, CI/CD pipelines, and Git-based development workflows.
Strong analytical, problem-solving, troubleshooting, communication, and stakeholder management skills.
Experience working in Agile development environments and collaborating with cross-functional teams.
Preferred experience with PySpark, Spark optimization, AWS Glue, Terraform or infrastructure as code, Kafka, Snowflake, REST APIs, Datadog, Prometheus, Grafana, machine learning and AI integration, healthcare, LLMs, and AI-enabled automation frameworks.