Data Automation Engineer (1157366)

The Judge Group; Washington (District of Columbia); On-site; USD 69,000 - 83,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Job summary

The Judge Group is seeking a Data Automation Engineer in Washington, DC to design scalable data automation workflows using AWS services and integrate with Azure data platforms where required.

You will develop ETL/ELT pipelines across DynamoDB, SQL Server on AWS, Azure SQL, and other sources, and build batch and near-real-time ingestion using Spark, Kafka, or Flume. You will evaluate GenAI services like Bedrock and Azure OpenAI for data enrichment and access.

Qualifications

• Bachelor’s degree in Computer Science or a related field.
• 5+ years of experience in data engineering, data automation, or a related discipline.
• Ability to design, develop, test, and troubleshoot Python- and SQL-based data pipelines in AWS environments with Azure integrations as needed.
• Strong hands-on experience with Apache Spark and at least one streaming/ingestion tech (Kafka or Flume).
• Hands-on experience with AWS data/integration services (S3, Glue, Lambda, EMR, Step Functions) and at least one AWS database service.
• Experience integrating at least one LLM platform (Bedrock, Azure OpenAI, etc.) into Python workflows.
• Experience with REST APIs and external services in Python data pipelines.
• Experience with Jira and CI/CD tools (GitHub, Azure DevOps, Jenkins).
• Strong troubleshooting/optimization skills across SQL, Spark, batch pipelines, and near-real-time ingestion workflows.
• US Citizenship and ability to obtain Federal government Public Trust clearance.

Responsibilities

• Design and implement scalable data automation workflows using AWS services with Azure integrations as needed.
• Develop ETL/ELT processes to ingest, transform, and move data across DynamoDB, SQL Server on AWS, and Azure SQL.
• Design and support batch and near-real-time ingestion pipelines using Spark, Kafka or Flume, and integrate with Apache Solr.
• Evaluate and apply GenAI services (Bedrock, Azure OpenAI, LangChain) for metadata enrichment, data-quality analysis, and natural-language data access.
• Develop scalable data-processing solutions using EMR and containerized deployments (Fargate, Kubernetes).
• Integrate Connect customer data into analytical data stores for reporting and analytics.
• Apply source-control, CI/CD, and containerization with GitHub, Azure DevOps, Jenkins, Docker.
• Implement data solutions with IAM, KMS, VPC isolation, RBAC, and firewall policies.
• Support Agile DevOps with sprint-based delivery of pipelines and AI-enabled features.

Skills

Python programming

SQL querying

Data pipeline design

Communication

Independent contributor

Education

Bachelor’s degree in Computer Science or related field

Tools

AWS

Azure

Apache Spark

Kafka

Flume

DynamoDB

SQL Server on AWS

Azure SQL

Amazon EMR

AWS Glue

AWS Lambda

AWS Step Functions

Jira

GitHub

Azure DevOps

Jenkins

Docker

Kubernetes

Apache Solr

Job description

Location: Washington, DC

Salary: $50.00 USD Hourly - $60.00 USD Hourly

Description: Our client is currently seeking a Data Automation Engineer

In This Role, You Will

• 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, such as metadata enrichment, data-quality analysis, structured data extraction, anomaly identification, and natural-language access to enterprise data. 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.

Required Qualifications

• Bachelor’s degree in Computer Science or a related field and 5+ years of experience in data engineering, data automation, or a related discipline.
• Candidates must be able to independently design, develop, test, and troubleshoot Python- and SQL-based data pipelines in AWS environments, including integrations with Azure services where required, and clearly explain their personal contribution to production implementations.
• 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.

Preferred Qualifications

• 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).

Medical, dental, and vision insurance are available to qualified candidates who meet eligibility requirements.