Senior Data Engineer (AWS | Spark | Python)

Richmond, VA, US • Posted 19 hours ago • Updated 19 hours ago
Contract W2
No Travel Required
On-site
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Amazon Redshift
  • Amazon S3
  • Artificial Intelligence
  • Amazon Web Services
  • Analytics
  • Apache Spark
  • Cloud Computing
  • Workflow
  • Snow Flake Schema
  • Streaming
  • Systems Design
  • Real-time
  • Reporting
  • SQL
  • SaaS
  • Screening
  • Performance Tuning
  • Problem Solving
  • Python
  • Query Optimization
  • Machine Learning Operations (ML Ops)
  • Orchestration
  • Electronic Commerce
  • Extract, Transform, Load
  • Machine Learning (ML)
  • Data Modeling
  • Data Quality
  • Decision-making
  • CaliberRM
  • Communication
  • Conflict Resolution
  • Data Architecture
  • Health Care
  • ELT
  • Apache Kafka
  • Terraform

Summary

Job Title: Senior Data Engineer (AWS | Spark | Python)
Location: Onsite 
Experience: 12+ Years (Strict)


Role Overview

We are seeking a high-caliber Data Engineer with deep expertise in building scalable, production-grade data platforms. This role is strictly for candidates who have hands-on experience designing and optimizing large-scale data pipelines in cloud environments.

You will work on high-impact data systems supporting analytics, AI/ML, and real-time decision-making. We are not looking for support-level or entry-level profiles—only engineers who have owned data systems end-to-end.


Must-Have Technical Skills (Non-Negotiable)

  • Strong programming expertise in Python (production-level coding)
  • Advanced SQL skills (query optimization, complex transformations)
  • Hands-on experience with Apache Spark (batch and/or streaming)
  • Deep experience with AWS:
  • S3, Glue, Redshift, Lambda
  • Building and maintaining ETL/ELT pipelines at scale
  • Experience with data modeling (star/snowflake schemas)
  • Working knowledge of distributed systems and large datasets

Preferred / High-Value Skills (Strong Advantage)

  • Experience with Kafka or real-time data streaming
  • Exposure to Snowflake / Databricks
  • Knowledge of Airflow or workflow orchestration tools
  • Experience supporting ML pipelines / MLOps workflows
  • Infrastructure as Code (Terraform)

Core Responsibilities

  • Design and build scalable, reliable data pipelines (batch & real-time)
  • Optimize data workflows for performance, cost, and reliability
  • Own end-to-end data architecture from ingestion to consumption
  • Collaborate with Data Scientists, Analysts, and Engineering teams
  • Ensure data quality, governance, and security standards
  • Troubleshoot complex data issues in production environments

What We Are Looking For

  • Engineers who have worked on production systems handling large-scale data
  • Strong problem-solving mindset with system design capability
  • Ability to write clean, efficient, and maintainable code
  • Proven experience in performance tuning and optimization
  • Clear communication and ownership mindset

Strict Screening Criteria (Important)

  • Reject profiles with only:
    • Basic SQL or reporting experience
    • No real cloud (AWS) exposure
    • No hands-on Spark experience
  • Prior experience in US-based projects or global clients is highly preferred

Nice-to-Have Industry Exposure

  • Finance / Healthcare / E-commerce / SaaS data platforms
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
  • Dice Id: 10477291
  • Position Id: 8948039
  • Posted 19 hours ago
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