AWS Data Engineer

Title: AWS Data Engineer Location: Newark, NJ (Hybrid) Duration: 12 Months Job Description: Responsibilities: Designing, building, and maintaining efficient, reusable, and reliable code Ensure the best possible performance and quality of high scale data applications and services Participate in system design discussions Independently perform hands on development and unit testing of the applications Collaborate with the development team and build individual components into enterprise data platform Work in a team environment with product, QE/QA, and cross functional teams to deliver a project throughout the whole software development cycle. Responsible to identify and resolve any performance issues Keep up to date with new technology development and implementation Participate in code review to make sure standards and best practices are met Project management: Agile developers take responsibility for estimating, planning, and managing all tasks and report on progress Software quality: The Agile developer is also responsible for the quality of the software he/she produces. The team takes responsibility for the quality of the work they produce instead of turning over some code to a separate and independent group for testing Teamwork: This includes collaboration with all other team members with the aim to take shared responsibility for the overall efforts Understanding user needs: This is about interacting with users as necessary to clarify requirements Primary & Must-Have Skills AWS Core Ecosystem: Glue, Lambda, S3, IAM Roles, Redshift, RDS. Infrastructure as Code (IaC): Writing CloudFormation templates. DevOps / CI-CD: GitHub repository version control, Jenkins, SonarQube, and X-Ray. Programming & Data Languages: Expert-level proficiency in Python (for Lambda) and SQL queries. Nice-to-Have Ecosystem Tools Candidates will build integrations between these ecosystem tools using the AWS tech stack. Prior familiarity with any of the following is a major plus: Informatica: Specifically Data Catalog, Data Lineage, and Data Marketplace. Denodo: For data virtualization and data semantics. Ataccama: For Master Data Management (MDM), Reference Data Management (RDM), and data quality observability. Background & Experience Profile Years of Experience: Around 8+ years of experience (Senior level). AWS Certifications: While strong experience is evaluated, certifications are highly preferred because they ensure a 360-degree visibility of AWS services for architectural design optimizations. Qualifications: Bachelor's degree in Computer Science, Software Engineering, MIS or equivalent combination of education and experience 8+ years of experience as Data Engineer on AWS Stack with experience on DevOps tool AWS Solutions Architect or AWS Developer Certification required Solid experience of AWS services such as CloudFormation, S3, Athena, Glue, Glue DataBrew, EMR/Spark, RDS, Redshift, DataSync, DMS, DynamoDB, Lambda, Step Functions, IAM, KMS, SM, EventBridge, EC2, SQS, SNS, LakeFormation, CloudWatch, Cloud Trail Implement high velocity streaming solutions and orchestration using Amazon Kinesis, AWS Managed Airflow and AWS Managed Kafka (preferred) Solid experience building solutions on AWS data lake/data warehouse Analyze, design, development, and implementation of data ingestion pipeline in AWS Knowledge implementing ETL/ELT for data solutions End-to-end data solutions (ingest, storage, integration, processing, access) on AWS Knowledge implementing RBAC strategy/solutions using AWS IAM and Redshift RBAC model Build & implement CI/CD pipelines for EDP Platform using CloudFormation and Jenkins Programming experience with Python, Shell scripting and SQL Knowledge of analyzing data using SQL Stored procedures Build automated data pipelines to ingest data from relational database systems, file systems, NAS shares to AWS relational databases such as Amazon RDS, Aurora, and Redshift Build Automated data pipelines to ingest data from Rest APIs to AWS data lake (S3) and relational databases such as Amazon RDS, Aurora, and Redshift. Good Experience of DevOps practice Experience with modern source code management and software repository systems (Bitbucket) Experience with programming languages (Python) Experience with scripting languages (Shell, Groovy) Experience with API deployment for tooling integration Experience using Jenkins, CloudBees (Pipeline as Code, Shared Libraries) The ability to document exceptions/issues/action plans/meeting minutes/lessons learned accurately and in a timely fashion Experience with administering DevOps tools in SaaS Experience using DevOps Tools (SonarQube, Artifactory etc.) Experience using build tools (Maven, MS Build and Gradle) Experience using containers (Docker) Experience using Atlassian suite (Jira, Confluence) Experience of Infrastructure as Code using CloudFormation Creating Jenkins CI pipelines to integrate Sonar/Security scans and test automation scripts Part of DevOps QA and AWS team focusing on building CI/CD pipeline Responsible for writing and maintaining Jenkins Pipelines