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Data Automation Engineer

Unisys

Remote • 14d ago

Easy Apply

Full-time

65 - 70

Data Engineer Intern (Data Platfrom TikTok BP) - 2027 Summer

TikTok

San Jose, California • Today

Full-time

USD 45.00 - 60.00 per hour

Software Integrator/Low Code Developer

Absolute Business Solutions Corp

Bethesda, Maryland • 5d ago

Full-time

USD 120,000.00 - 245,000.00 per year

Data Engineer Intern (E-commerce) - 2027 Summer

TikTok

San Jose, California • Today

Full-time

USD 45.00 - 60.00 per hour

Data Engineer Graduate (Data Platform E-commerce) - 2027 Start

TikTok

San Jose, California • Today

Full-time

USD 128,000.00 - 316,800.00 per year

Data Engineer Graduate (Data Platfrom TikTok BP) - 2027 Start

TikTok

San Jose, California • Today

Full-time

USD 128,000.00 - 316,800.00 per year

Senior Architect, Data Engineering

West Monroe

Chicago, Illinois • Today

Full-time

USD 199,100.00 - 223,400.00 per year

Data Automation Engineer

Robert Half

Remote or Washington, District of Columbia • Today

Easy Apply

Contract

USD 48.00 - 54.00 per hour

Senior Software Engineer

Kohls Department Stores

Menomonee Falls, Wisconsin • Today

Full-time

SDC Java Software Developer---Staff/Analyst---St. Louis

St. Louis, Missouri • Today

Full-time

USD 55,400.00 - 113,000.00 per year

SDC Java Software Developer---Senior---St. Louis

St. Louis, Missouri • Today

Full-time

USD 67,000.00 - 136,800.00 per year

Senior Software Engineer, Big Data

ZipRecruiter

Santa Monica, California • Today

Full-time

USD 170,000.00 - 240,000.00 per year

SDC-Java Developer: Full Stack or Back End---Analyst---Raleigh, Dallas, San Antonio

Dallas, Texas • Today

Full-time

USD 55,400.00 - 113,000.00 per year

Senior/Principal Software Engineer - Creator Data

Roblox

San Mateo, California • Today

Full-time

USD 345,040.00 - 399,420.00 per year

Job Details

Skills

Amazon Web Services
Amazon EMR
Amazon DynamoDB
Amazon S3
HIPAA
Python
Microsoft SQL Server
Microsoft
Artificial Intelligence
Amazon Lambda
Apache Kafka
Apache Flume
Extract, Transform, Load
Continuous Delivery
Continuous Integration
Open Source
SQL Azure
Terraform
Lifecycle Management
Machine Learning (ML)

Summary

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.

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).

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: RTL61687
Position Id: 9115656
Posted 14 days ago

Fitment

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Contact the job poster

Syed Adnan Jaffer

Senior Talent Acquisition Specialist @ Unisys

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Job Details

Skills

Amazon Web Services
Amazon EMR
Amazon DynamoDB
Amazon S3
HIPAA
Python
Microsoft SQL Server
Microsoft
Artificial Intelligence
Amazon Lambda
Apache Kafka
Apache Flume
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Summary

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.

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).

Dice Id: RTL61687
Position Id: 9115656
Posted 14 days ago

Fitment