GuidehouseRemote
AWS Data Engineer
United States of America
Full Time
5 days ago
$89,000 - $148,000 USD
No H1B
Key skills
AWS S3AWS GlueAWS DMSAWS AthenaAWS RedshiftAWS EMRAWS KinesisAWS LambdaPythonScalaJavaSQLPostgreSQLOracleDynamoDBDocumentDBData modelingTerraformCloudFormationAWS CDKCI/CD pipelinesNoSQLETLELTData EngineeringAnalyticsBITableauPower BIRedshiftSparkApache SparkAirflowApache AirflowAWSKubernetesDockerECSEKSLambdaS3RDSGlueAthenaKinesisGitVersion ControlCI/CD
About this role
Guidehouse is a company seeking an experienced AWS Data Engineer to design, build, and maintain robust data pipelines in the AWS cloud environment. The role involves collaborating with database administrators and data scientists to deliver scalable data solutions and ensuring optimal performance and reliability of data processing solutions.
Responsibilities:
- We are seeking an experienced AWS Data Engineer to design, build, and maintain robust data pipelines, and data processing solutions in the AWS cloud environment
- This role will work closely with our AWS Cloud DBA to create end-to-end data solutions to migrate data from legacy application to AWS cloud platform ensuring optimal performance and reliability
- The ideal candidate has hands-on experience with AWS data services, modern data architecture patterns, and can collaborate with database administrators, data scientists, and business stakeholders to deliver scalable data solutions
- Design and implement scalable data pipelines using AWS services including AWS Glue, Step Functions, Lambda, and Kinesis
- Build ETL/ELT processes to ingest, transform, and load data from various sources into data warehouses and data lakes
- Architect and maintain data lakes using S3, implement data cataloging with AWS Glue Catalog, and optimize data storage formats (Parquet, Delta, etc.)
- Design data warehouse solutions using Redshift and integrate with existing RDS/Aurora databases managed by the DBA team
- Develop real-time and batch data processing solutions using Kinesis Data Streams, Kinesis Analytics, EMR, and AWS Batch
- Create and maintain data models, schemas, and documentation
- Work with the DBA team to optimize data access patterns and query performance across relational and analytical databases
- Build automated data quality checks, monitoring, and alerting systems
- Implement data governance policies and ensure compliance with data retention, privacy, and security requirements
Requirements:
- US Citizenship or Green Card Holder
- A Bachelor's degree or additional four(4) years of relevant experience will be needed in lieu of degree
- A minimum of Four (4) to Eight(8) years of experience in data engineering with at least 2+ years working in AWS cloud environments
- Strong expertise in AWS data services, including S3, Glue, DMS, Athena, Redshift, EMR, Kinesis, and Lambda
- Proficiency in programming languages, including Python, Scala, or Java, for data processing and pipeline development
- Experience with SQL and working knowledge of both relational databases (PostgreSQL, Oracle) and NoSQL systems (DynamoDB, DocumentDB)
- Understanding of data modeling concepts for both transactional and analytical workloads
- Experience with Infrastructure as Code tools (Terraform, CloudFormation, CDK) and CI/CD pipelines for data engineering workflows
- Strong analytical and problem-solving skills with attention to data quality and system reliability
- Proven ability to work collaboratively with database administrators, data scientists, and business stakeholders
- No visa sponsorship available for this position
- Must be able to OBTAIN and MAINTAIN a Federal or DoD 'PUBLIC TRUST'; candidates must obtain approved adjudication of their PUBLIC TRUST prior to onboarding with Guidehouse
- AWS Certified Data Engineer – Associate certification (Highly desirable)
- Candidates with an ACTIVE PUBLIC TRUST or SUITABILITY are preferred
- AWS Certified Solutions Architect or other relevant AWS certifications
- Familiarity with big data frameworks such as Apache Spark, Apache Airflow, or AWS-native orchestration tools
- Knowledge of data formats (JSON, Avro, Parquet, ORC) and compression techniques for optimal storage and processing
- Familiarity with version control systems (Git) and collaborative development practices
- Knowledge of container orchestration (Docker, Kubernetes, ECS, EKS)
- Experience with data visualization tools (QuickSight, Tableau, Power BI)
- Understanding of data privacy regulations (GDPR, CCPA) and compliance frameworks
- Experience with performance tuning and optimization of distributed data processing systems