Data Engineer

JSR Tech Consulting

Newark, NJ, United States

6 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.thejobnetwork.com

Tech stack

Airflow Amazon Web Services Amazon Elastic Compute Cloud Amazon S3 Data Analysis Computer Programming Continuous Delivery Continuous Integration Extract Transform Load (ETL) Data Systems Relational Databases File Systems

+26 more

Amazon DynamoDB Identity and Access Management Python (Programming Language) Network Attached Storage (Server Appliance) Role-Based Access Control Shell Script Amazon Simple Notification Service (SNS) Software Engineering SQL Stored Procedures SQL Databases DevOps Tools - Open-source Delivery Pipeline Apache Spark Cloudformation Servicebus Amazon Relational Database Service Information Technology Data Lineage AWS Data Analytics Apache Kafka SAP Ariba Cloudwatch Amazon Simple Queue Service (SQS) Marketplace Data Pipelines Jenkins

Job description

Join a major investment firm as a Senior AWS Data Engineer supporting enterprise data platform initiatives. This role focuses on building scalable data pipelines, CI/CD automation, and end-to-end data solutions across the AWS ecosystem. Experience with Informatica (data catalogue, marketplace, data lineage, data quality), Denodo, and/or Ataccama is a strong plus.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, MIS, or equivalent combination of education and experience \n
8+ years of experience as a Data Engineer on the AWS stack, including DevOps tooling \n
AWS Solutions Architect or AWS Developer Certification required \n
Strong hands-on experience with AWS services, including: \n
CloudFormation, S3, Athena, Glue, Glue DataBrew, EMR/Spark \n
RDS, Redshift, DynamoDB, Lambda, Step Functions \n
IAM, KMS, SM, EventBridge, EC2, SQS, SNS \n
LakeFormation, CloudWatch, CloudTrail, DataSync, DMS \n
Experience implementing high-velocity streaming solutions using Amazon Kinesis, AWS Managed Airflow, and AWS Managed Kafka (preferred) \n
Proven experience building AWS data lake/data warehouse solutions \n
Skilled in designing, developing, and implementing data ingestion pipelines on AWS \n
Knowledge of ETL/ELT implementation for data solutions \n
Experience delivering end-to-end data solutions: ingest, storage, integration, processing, and access \n
Familiarity implementing RBAC strategies using AWS IAM and Redshift RBAC model \n
Experience building and implementing CI/CD pipelines using CloudFormation and Jenkins \n
Strong programming skills in Python, Shell scripting, and SQL \n
Experience with SQL stored procedures for data analysis

Benefits & conditions

Experience building automated pipelines to ingest data from relational databases, file systems, and NAS shares into AWS RDS, Aurora, and Redshift \n
Experience building automated pipelines to ingest data from REST APIs into AWS S3 and relational databases \n
Solid DevOps background, including: \n
Bitbucket (source code management) \n
Python, Shell, Groovy scripting \n
API deployment for tooling integration \n
Jenkins and CloudBees (Pipeline as Code, Shared Libraries) \n
SonarQube, Artifactory, and similar DevOps tools \n
Maven, MS Build, Gradle \n
Docker \n
Jira and Confluence \n
Infrastructure as Code via CloudFormation \n
Experience creating Jenkins CI pipelines integrating Sonar/security scans and test automation \n
Ability to accurately document exceptions, issues, action plans, meeting minutes, and lessons learned \n