Data Engineer

GM Financial

. Work with internal business partners to identify, capture, collect, and format data from external sources, internal systems, and the data warehouse .

Posted 10/16/2026full-timeArlington • Texas • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and managing large-scale data pipelines using technologies such as Hadoop, Spark, and cloud platforms. Proficient in data privacy, governance, and integrating AI tools to enhance data workflows and analytics.

Highest-signal resume keywords

Large-Scale Data ManagementETL/Data Pipeline DevelopmentCloud Platform ExperienceData Privacy and GovernanceAI Integration in Workflows

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

HadoopSparkKafkaSQLNoSQLETLData PipelinesJSONParquetDocker

Soft Skills

Problem-SolvingTroubleshootingWritten CommunicationVerbal Communication

Tools & Technologies

AzureAWSGoogle CloudTerraformKubernetesAdobe Experience PlatformREST APIsCI/CDGitAI Tools

Industry Keywords

Data PrivacyGDPRAgileScrumData LakesLakehousesStreaming PipelinesData GovernanceInfrastructure-as-CodeRegulatory Compliance

Tech Stack

Tools & technologies

AWSAzureCassandraCloudDockerETLHadoopHDFSKafkaKubernetesMongoDBNoSQLRedisSparkSQLTerraform

About the role

Key responsibilities & impact

Work with internal business partners to identify, capture, collect, and format data from external sources, internal systems, and the data warehouse
Extract features of interest from collected data
Contribute to evaluation, research, and experimentation with batch and streaming data engineering technologies
Keep pace with industry innovation through lab-based technology exploration
Showcase emerging technology capabilities and enable adoption of new technologies and techniques
Coordinate with Privacy Compliance to ensure proper data collection and handling
Create and implement business rules and functional enhancements for data schemas and processes
Perform data load monitoring and resolution
Problem-solve data availability and activation issues with internal business clients
Design, integrate, and manage scalable data pipelines supporting business and analytics needs

Requirements

What you’ll need

Experience designing, processing, and managing large-scale data using Hadoop, Spark, Kafka, HDFS, or similar distributed platforms
Hands-on experience with Azure, AWS, or Google Cloud
Experience with infrastructure-as-code tools such as Terraform, ARM Templates, or CloudFormation
Experience building and maintaining ETL/data pipelines
Experience working with JSON, Parquet, relational databases, cloud databases, and messaging platforms
Understanding of data lakes, lakehouses, streaming pipelines, ADLS Gen2, and Amazon S3
Experience with SQL and NoSQL technologies such as Cosmos DB, MongoDB, Cassandra, or Redis
Experience with Docker and Kubernetes
Familiarity with Git, CI/CD, build tools, code quality tools, and artifact management
Experience with Adobe Experience Platform, Real-Time CDP, Journey Analytics, tag management platforms, SDKs, and REST APIs
Understanding of data privacy, security, governance, regulatory considerations, and GDPR
Experience working in Agile environments using Scrum, SAFe, or similar methodologies
Strong problem-solving and troubleshooting skills
Strong written and verbal communication skills
Ability to use AI tools such as Microsoft Copilot
Ability to evaluate AI outputs for accuracy, compliance, and bias
Experience integrating AI into workflows
Familiarity with AI-assisted research, summarization, and content generation
Understanding of responsible AI use, ethics, and data protection
High School Diploma or equivalent required
Bachelor’s Degree in a related field or equivalent experience required
Must not require visa sponsorship now or in the future

Benefits

Comp & perks

401K matching
Bonding leave for new parents: 12 weeks, 100% paid
Tuition assistance
Training
GM employee auto discount
Community service pay
Nine company holidays
Bonus eligibility
Flexible hybrid work environment