Data Automation Engineer

Job Summary This•Washington, DC

21h•Remote

About The Position

This is a fully remote, teleworking position with potential travel to the Washington D.C. metro area on special occasions. The role involves designing and implementing data automation solutions primarily on AWS, with integration to selected Azure services and Generative AI capabilities. The candidate will be responsible for building scalable data pipelines and automation solutions that integrate cloud services, enterprise tools, and Generative AI to support mission-critical analytics, reporting, and customer engagement platforms. The ideal candidate is mission-focused, delivery-oriented, and able to apply critical thinking to design practical solutions and resolve complex technical issues.

Requirements

Bachelor’s degree in Computer Science or a related field and 5 years of experience in data engineering, data automation, or a related discipline.
Ability to independently design, develop, test, and troubleshoot Python- and SQL-based data pipelines in AWS environments, including integrations with Azure services where required.
Strong hands-on experience with Apache Spark and working knowledge of at least one streaming or ingestion technology, such as Apache Kafka or Apache Flume.
Hands-on experience with multiple AWS data and integration services, including several of the following: Amazon S3, AWS Glue, AWS Lambda, Amazon EMR, AWS Step Functions, and at least one AWS database service.
Practical experience integrating at least one LLM platform or model service, such as Amazon Bedrock, Azure OpenAI Service, or an open-source model, into a Python-based workflow.
Experience integrating REST APIs and external services into Python-based data pipelines and automated workflows.
Experience using Jira and one or more source-control, build, or CI/CD platforms, such as GitHub, Azure DevOps, or Jenkins.
Strong troubleshooting and performance-optimization skills across SQL, Spark, batch pipelines, and near-real-time ingestion workflows.
Experience supporting production data platforms, including SLA monitoring, incident resolution, root-cause analysis, data reconciliation, performance troubleshooting, vulnerability remediation, and recurring maintenance.
Good communication and presentation skills.
US Citizenship and ability to obtain Federal government Public Trust clearance.

Nice To Haves

Relevant certifications, such as AWS Certified Data Engineer – Associate, AWS Certified Machine Learning – Specialty, Microsoft Certified: Azure AI Engineer Associate, or Databricks Certified Data Engineer.
Familiarity with retrieval-augmented generation pipelines, embeddings, and vector-search technologies such as Apache Solr, Amazon OpenSearch Service, pgvector, or similar platforms.
Experience with multi-cloud data integration (AWS and Azure).
Experience with Docker and Kubernetes for containerized deployment, scalable data processing, and orchestration.
Experience operationalizing Generative AI workflows, including prompt and model configuration, evaluation, observability, monitoring, and lifecycle management.
Knowledge of data lineage/governance tools (Purview, Unity Catalog, AWS Glue Catalog).
Familiarity with infrastructure-as-code tools, such as Terraform, AWS CloudFormation, or Bicep, for automated deployments.
Experience with compliance frameworks (FedRAMP, PCI-DSS, HIPAA).

Responsibilities

Design and implement scalable data automation workflows using AWS services, with integration to selected Azure data platforms where required.
Develop ETL/ELT processes to ingest, transform, and move data across Amazon DynamoDB, SQL Server hosted on AWS, Azure SQL, and other enterprise data sources.
Design, develop, and support batch and near-real-time ingestion pipelines using Apache Spark and technologies such as Kafka or Flume, and collaborate with the search engineering team to integrate those pipelines with the existing Apache Solr platform.
Evaluate and apply Generative AI services and frameworks, such as Amazon Bedrock, Azure OpenAI, Hugging Face, and LangChain, to prototype and evaluate selected GenAI-assisted capabilities.
Recommend suitable use cases for future implementation.
Develop scalable data-processing solutions using Amazon EMR and containerized deployment environments such as AWS Fargate or Kubernetes.
Integrate Amazon Connect customer-interaction data into analytical data stores for operational reporting and analytics.
Apply source-control, build, containerization, and CI/CD practices using tools such as GitHub, Azure DevOps, Jenkins, and Docker.
Implement data solutions in accordance with established security and compliance controls, including identity and access management, KMS encryption, VPC isolation, role-based access control, and firewall policies.
Support Agile DevOps processes with sprint-based delivery of pipeline and AI-enabled features.