Data Engineer

VALUE SPECTRUM TECHNOLOGIES LLC
Phoenix, United States of America
yesterday

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Phoenix, United States of America

Tech stack

Java
Airflow
Amazon Web Services (AWS)
Amazon Web Services (AWS)
Big Data
Code Review
Databases
Continuous Integration
Information Engineering
Data Governance
Data Integration
ETL
Data Security
Data Vault Modeling
Data Warehousing
DevOps
Dimensional Modeling
Amazon DynamoDB
Github
Hadoop
Monitoring of Systems
Hive
Identity and Access Management
Python
PostgreSQL
Machine Learning
Microsoft SQL Server
MongoDB
MySQL
Oracle Applications
Cloud Services
SQL Databases
Datadog
Real Time Systems
Sql Optimization
Snowflake
Spark
AWS Lambda
GIT
Cloudformation
Amazon Web Services (AWS)
Data Lake
PySpark
Kubernetes
Infrastructure Automation Frameworks
Information Technology
Amazon Web Services (AWS)
Star Schema
Integration Frameworks
Amazon Web Services (AWS)
Kafka
Data Management
Cloudwatch
Terraform
Splunk
Data Pipelines
Amazon Web Services (AWS)
Docker
ELK
Jenkins
Redshift
Programming Languages

Job description

We are seeking an experienced Senior Data Engineer with 10+ years of expertise in designing, developing, and maintaining scalable data platforms and data pipelines. The ideal candidate should possess strong hands-on experience in AWS cloud services, big data technologies, ETL/ELT frameworks, data warehousing, and modern data engineering practices. The candidate will work closely with business stakeholders, data architects, data scientists, and application teams to build robust, high-performance, and secure data solutions that support analytics, reporting, and machine learning initiatives., * Design, develop, and maintain scalable data pipelines for batch and real-time processing.

  • Build cloud-native data solutions using AWS services and modern data engineering frameworks.
  • Develop ETL/ELT processes to ingest, transform, and load data from multiple structured and unstructured data sources.
  • Design and implement data lakes and data warehouses for enterprise-scale analytics.
  • Optimize data processing workflows for performance, reliability, and cost efficiency.
  • Implement data quality checks, validation frameworks, and monitoring solutions.
  • Collaborate with Data Scientists, BI teams, and business stakeholders to support analytics requirements.
  • Develop reusable data frameworks and automation solutions.
  • Implement security, governance, and compliance controls across data platforms.
  • Troubleshoot production issues and perform root cause analysis.
  • Mentor junior and mid-level data engineers.
  • Participate in architecture reviews, code reviews, and best-practice initiatives.
  • Support CI/CD implementations and infrastructure automation.

Required Technical Skills AWS Cloud Services AWS Glue, Amazon S3, AWS Lambda, Amazon Redshift, Amazon EMR, Amazon Athena, AWS Lake Formation, AWS Step Functions, Amazon Kinesis, Amazon RDS, Amazon DynamoDB, Amazon CloudWatch, AWS IAM, AWS Data Pipeline Data Engineering Technologies

  • Apache Spark (PySpark/Spark SQL)
  • Hadoop Ecosystem
  • Apache Kafka
  • Apache Airflow

Programming Languages

  • Python (Advanced)
  • SQL (Expert Level)
  • Scala (Preferred)
  • Java (Preferred)

Database Technologies

  • SQL Server
  • Oracle
  • PostgreSQL
  • MySQL
  • Snowflake
  • Redshift
  • MongoDB

Data Warehousing

  • Star Schema
  • Snowflake Schema
  • Data Modeling
  • Dimensional Modeling
  • Data Vault

DevOps & Automation

  • Terraform
  • CloudFormation
  • Git
  • Jenkins
  • GitHub Actions
  • Docker
  • Kubernetes

Monitoring & Observability

  • CloudWatch
  • Splunk
  • ELK Stack
  • Datadog

Requirements

  • Bachelor''s or Master''s Degree in Computer Science, Information Technology, Engineering, or related field.
  • 10+ years of overall IT experience.
  • 7+ years of Data Engineering experience.
  • 5+ years of hands-on AWS cloud experience.
  • Strong expertise in building large-scale data processing systems.
  • Extensive experience with ETL/ELT frameworks and data integration.
  • Advanced SQL and Python programming skills.
  • Experience working in Agile/Scrum environments.
  • Strong understanding of data governance, security, and compliance standards.

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