Data Engineer with Green-Field - Build-from-Scratch Experience

Montgomery, AL, US • Posted 1 day ago • Updated 1 hour ago
Contract W2
Contract Independent
12 Months
No Travel Required
On-site
Depends on Experience
Fitment

Dice Job Match Score™

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Job Details

Skills

  • Change Management
  • Cloud Computing
  • Data Architecture
  • Data Engineering
  • Data Governance
  • Data Management
  • Data Quality
  • ELT
  • Extract
  • Transform
  • Load
  • KPI
  • Lifecycle Management
  • Master Data Management
  • Mobile Device Management
  • Optimization
  • Performance Tuning
  • Recruiting
  • Regulatory Compliance
  • Roadmaps
  • SQL
  • Scalability
  • Scratch
  • System Integration

Summary

DAY 1 ONSITE OPPORTUNITY 

Role Overview :

The Hiring Manager is specifically looking for a senior, hands-on Data Quality/Data Engineering leader who has built enterprise data quality and governance ecosystems in green-field or low-maturity environments — not someone who only worked as a small contributor within a large established data organization.

  • What the HM Really Wants
  • Green-Field / Build-from-Scratch Experience
  • Must have experience creating data quality and governance processes from the ground up

Should understand:

  • what tools are needed,
  • why they are needed,
  • when they should be implemented,
  • and how they integrate into enterprise ecosystems
  • Needs someone who can “build the wheel,” not just operate within an existing framework
  • Deep Enterprise Data Management Expertise

Strong understanding of:

  • Enterprise data architecture
  • Data governance programs
  • Data compliance frameworks
  • Master Data Management (MDM)
  • Change management processes
  • Data lifecycle management
  • Candidate should be able to explain both:
  • strategic “30,000-foot” enterprise vision
  • detailed implementation-level execution
  • Strong Backend Technical Expertise

Must have:

  • Advanced backend SQL experience
  • Deep ETL/ELT architectural knowledge

Ability to explain:

  • architectural tradeoffs, optimization strategies, performance tuning, scalability considerations, and complex transformation logic, Data Quality Leadership

Should know how to:

  • Define enterprise-wide data quality standards
  • Implement governance frameworks
  • Establish monitoring and observability
  • Create phased implementation roadmaps with timelines

Define KPIs for:

  • accuracy, completeness, consistency, timeliness, compliance, Tooling & Platform Strategy

Needs hands-on experience selecting and implementing:

  • Data quality tools
  • Governance platforms
  • Observability solutions
  • Cloud data platforms
  • MDM systems
  • Integration frameworks
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: 91113681
  • Position Id: 232435143
  • Posted 1 day ago
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