> Markdown version of [/jobs/ext/2834759-data-engineer](https://www.wearedevelopers.com/jobs/ext/2834759-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Engineer - **Company:** Findem, Inc. - **Location:** Plano, TX, United States - **Contract:** Permanent contract - **Skills:** Airflow, Amazon Web Services, Amazon S3, Code Review, Information Engineering, Data Integration, Extract Transform Load (ETL), Data Transformation, Data Mining, Data Systems, Database Queries, Identity and Access Management, Python (Programming Language), NoSQL, Cloud Services, Workflow Management Systems, Software Organization, Data Logging, Data Processing, Apache Spark, Git, Cloudformation, Data Lakes, Pyspark, Infrastructure Automation Frameworks, Deployment Automation, AWS Glue, Data Management, Functional Programming, Cloudwatch, Terraform, Data Pipelines - **Published:** September 10, 2026 - **Apply:** https://www.dice.com/job-detail/7a98832c-cd9d-4776-b8dc-c91616470f2e ## About the Role The successful candidate will work on building scalable data pipelines, integrating data from multiple sources, and developing reliable data processing workflows. This role requires strong problem-solving skills and the ability to work collaboratively in a fast-paced environment., * Strong professional experience with Python development, particularly for data engineering and ETL applications. * Hands-on experience developing ETL/ELT pipelines. * Strong experience with AWS cloud services, particularly data engineering services. * Hands-on experience with AWS Glue. * Strong experience with Apache Airflow and workflow orchestration. * Experience working with S3, Lambda, IAM, CloudWatch, and other AWS services. * Strong understanding of data transformation and pipeline architecture. * Experience working with relational and/or NoSQL databases. * Strong SQL skills. * Experience with Git and modern software development practices. * Strong troubleshooting and problem-solving abilities. * Excellent communication and collaboration skills. Preferred Qualifications * Experience with PySpark and Spark-based data processing. * Experience designing data lakes or modern cloud data platforms on AWS. * Experience with CI/CD pipelines and automated deployments. * Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation. * Experience working in Agile/Scrum development environments. * Experience with data quality, governance, and monitoring frameworks. Work Location This position is primarily based out of the McLean, VA office. The team may also consider candidates who can work from the Plano, TX location. Candidates should be comfortable working in a collaborative, fast-paced environment and adapting to changing project priorities. ## Description We are seeking an experienced Data Engineer to join our data engineering team. The ideal candidate will have strong hands-on experience developing and supporting ETL pipelines using Python and AWS, with a particular focus on AWS Glue and Apache Airflow., * Design, develop, and maintain scalable ETL/ELT data pipelines using Python and AWS technologies. * Develop and optimize data processing workflows using AWS Glue. * Build, schedule, monitor, and troubleshoot data pipelines using Apache Airflow. * Write efficient, maintainable, and reusable Python code for data extraction, transformation, and loading. * Work with AWS services to build secure, scalable, and highly available data solutions. * Integrate data from various structured and unstructured sources. * Perform data transformation, cleansing, validation, and quality checks. * Troubleshoot pipeline failures, performance issues, and data discrepancies. * Implement logging, monitoring, alerting, and error-handling mechanisms for production pipelines. * Collaborate with data engineers, architects, application teams, and business stakeholders. * Participate in code reviews and follow established development and deployment standards. * Contribute to continuous improvement of existing data engineering processes and infrastructure.