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AWS Cloud Data Engineer

System One McLean, VA 🇺🇸 Remote

McLean, VA

Today

Contract Amazon Emr Amazon Rds Amazon Web Services

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Job Description

System One is seeking an AWS Cloud Data Engineer for a remote, long-term contract in McLean, VA. This role centers on designing, building, and maintaining scalable data pipelines in AWS, with emphasis on ETL/ELT, data modeling, governance, and data quality. The position requires the ability to obtain a Federal Public Trust clearance.

Responsibilities

Design, build, and sustain scalable data pipelines and processing solutions within AWS environments.
Collaborate with AWS Cloud DBAs and cross-functional teams to migrate data from legacy applications to AWS, ensuring performance, reliability, and security.
Develop ETL and ELT workflows to ingest, transform, and load data into data lakes, data warehouses, and analytical platforms.
Leverage AWS data services such as S3, Glue, Step Functions, Lambda, Kinesis, EMR, Athena, Redshift, RDS, and Aurora.
Create and maintain data models, schemas, documentation, and data access patterns for both transactional and analytical workloads.
Implement data quality checks, monitoring, governance controls, and compliance practices for data retention, privacy, and security requirements.

Requirements

Ability to obtain a Federal Public Trust clearance.
Bachelor's degree in Computer Science, Data Engineering, or a related field; four additional years of relevant experience may substitute for a degree.
Minimum of 6 years of data engineering experience, including at least 3 years working in AWS cloud environments.
Strong experience with AWS data services including S3, Glue, DMS, Athena, Redshift, EMR, Kinesis, and Lambda.
Proficiency in Python, Scala, or Java for data processing and pipeline development.
Experience with SQL and relational databases such as PostgreSQL or Oracle, and NoSQL databases such as DynamoDB or DocumentDB.
Understanding of data modeling concepts for transactional and analytical workloads.
Experience with Infrastructure as Code tools such as Terraform, CloudFormation, or CDK, and CI/CD pipelines for data engineering workflows.
Strong analytical, problem-solving, collaboration, and communication skills with a focus on data quality and system reliability.

Technologies

S3
Glue
Step Functions
Lambda
Kinesis
EMR
Athena
Redshift
RDS
Aurora
Python
Scala
Java
SQL
PostgreSQL
Oracle
DynamoDB
DocumentDB
Terraform
CloudFormation
CDK
Apache Spark
Apache Airflow
Git
Docker
Kubernetes
ECS
EKS
QuickSight
Tableau
Power BI
JSON
Avro
Parquet
ORC

Benefits

Medical insurance
Dental insurance
Vision insurance
Spending accounts
Life insurance
Voluntary plans
401(k) plan

NICE TO HAVE

AWS Certified Data Engineer – Associate certification
AWS Certified Solutions Architect or other relevant AWS certifications
Experience with Apache Spark, Apache Airflow, or AWS-native orchestration tools
Knowledge of data formats such as JSON, Avro, Parquet, and ORC, including compression techniques
Familiarity with Git and collaborative development practices
Knowledge of container technologies including Docker, Kubernetes, ECS, or EKS
Experience with data visualization tools such as QuickSight, Tableau, or Power BI
Understanding of data privacy regulations and compliance frameworks
Experience tuning and optimizing distributed data processing systems
Knowledge of networking, security, and IAM policies related to data engineering workflows

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19d ago

Reliability Analytics Engineer

Amazon.com Services LLC Seattle, WA

Seattle, WA

19d ago

$117k - $160k/yr Full time Amazon Emr Amazon Rds Amazon Web Services

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Job Description

Amazon.com Services LLC is seeking a Reliability Analytics Engineer to blend hands-on reliability work with a strong data systems mindset. The role is onsite in Seattle, WA, with a salary range of USD 117,300 to 160,000 per year. This position concentrates on reliability data analytics, defining data pipelines, and building tools and dashboards to support reliability across Amazon Robotics fleets, scaling evaluation across multiple product programs.

Responsibilities

Articulate data requirements and specifications for reliability pipelines, partnering with data engineering teams to design and validate ETL processes that ingest field failures, test data, and product telemetry from enterprise data lakes and EAM systems.
Cleanse, validate, and prepare reliability datasets (including censored life data, field service records, and accelerated test results) to ensure accurate assessments before they inform engineering decisions.
Translate reliability engineering questions into data queries and assessment workflows, converting ambiguous problems into repeatable, scalable evaluation methods that other engineers can reuse.
Develop and maintain reliability-focused assessment tools and automation using Python, AI/ML, and statistical libraries, such as automated failure mode classification, survival assessment calculators, and fleet health monitoring scripts.
Define dashboard requirements for reliability KPIs (availability, MTBF, MTTR, and failure rate trends); build prototypes and operationalize production dashboards.
Identify data gaps, define collection requirements for new failure modes or test programs, and collaborate with hardware test and field teams to close data gaps.
Establish data governance practices for reliability datasets, including metadata standards, version control, and traceability to source systems to ensure reproducible and auditable assessment findings.

Requirements

BS degree in mechanical engineering or equivalent
3+ years of experience in mechanical engineering or equivalent
Experience with data analysis tools such as Advanced Excel, SQL, Tableau, Python
Experience applying basic statistical methods (e.g., regression) to challenging business problems

Technologies

Python
SQL
Tableau
Advanced Excel
AWS S3
AWS Redshift
AWS EMR
AWS RDS
Reliasoft
Minitab
JMP
AI/ML

Benefits

Medical, Dental, and Vision Coverage
Maternity and Parental Leave Options
Paid Time Off (PTO)
401(k) Plan

A Day In The Life

Run Mean Cumulative Function (MCF) and Weibull analyses on robot fleets to verify suspensions before sharing results with the reliability team.
Collaborate with data engineers to define telemetry pipeline requirements, including required fields, censoring rules, and output schemas.
Develop Python tools that automate reliability growth tracking from the data lake, apply models, and generate standard reports accessible to engineers.
Provide failure rate data by subsystem to support DFMEA exercises.
Refine machine learning classifiers that auto-tag field failure tickets by mode, improving accuracy with sustaining team feedback.
Operate as a reliability engineer who understands repairable systems, physics-of-failure, and life data.
Differentiate through scaling expertise by building data tools, automation, and AI that the entire team can run。

About The Team

Amazon Robotics Reliability Engineering is a multidisciplinary group of engineers united by a drive to astonish customers by solving complex optimization challenges. The team thrives on ambiguity, moves quickly, and supports one another’s growth.

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