Proper Staffing Strategy

Abstract

• The Data Office should blend senior leaders, technical mid-level staff, and entry-level talent for scale and sustainability.
• Roles include CDO, Data Governance Lead, Stewards, Architects, Engineers, and Analysts.
• Skills range from data policy and architecture to cataloging, privacy, and quality management.
• Recruiting strategy includes internal upskilling, external specialists, and university partnerships.
• Use internships, hackathons, and capstone projects to engage early-career talent affordably.
• Estimated staffing cost: $1.6M–$1.8M per year, scalable with business growth.

Objective of Staffing Strategy

To build a high-performing, lean, and budget-conscious Data Office team by strategically combining senior leadership, mid-level specialists, and early-career talent from top universities and programs, ensuring capacity to scale data governance, analytics, and compliance across the organization.

Benefits

• Balanced expertise across strategy, operations, and technical implementation
• Cost efficiency by supplementing full-time hires with interns and apprentices
• Strong pipeline of future full-time employees from campus programs
• Agile team capable of piloting, scaling, and maintaining enterprise-wide data initiatives
• Cross-functional collaboration between IT, Legal, Product, and Engineering teams

Ideal Staffing Structure & Profiles

1. Chief Data Officer (CDO)

• Level: Senior Executive (10+ years)
• Skills: Leadership, data strategy, stakeholder engagement, regulatory awareness
• Recruiting Strategy: Internal promotion from IT/Innovation leadership or external search via executive recruiters (focus on automotive, manufacturing, or consulting backgrounds)

2. Data Governance Lead

• Level: Senior Manager
• Skills: Data policy writing, compliance frameworks (GDPR, ISO), data stewardship frameworks, program management
• Recruiting Strategy:
  • Target mid-level professionals from consulting firms (e.g., Deloitte, Accenture)
  • Consider alumni from government or regulatory bodies for strong compliance background
  • Use LinkedIn and data governance communities (e.g., DAMA, DCAM) for outreach

3. Data Stewards (3–5)

• Level: Entry to Mid-Level
• Skills: Data profiling, domain knowledge (manufacturing, sales, engineering), issue tracking, metadata documentation
• Recruiting Strategy:
  • Partner with domain managers to identify internal candidates (e.g., business analysts)
  • Launch a rotational program for junior hires from internal IT or Operations
  • Recruit recent graduates from top data programs (e.g., Vanderbilt, MTSU for locals, Georgia Tech, Michigan, Stanford, etc. for national reach) with co-op or capstone experience

4. Data Architect

• Level: Senior
• Skills: Data modeling, system integration, enterprise architecture, cloud platforms (Azure, AWS, GCP)
• Recruiting Strategy:
  • Source from senior engineers or architects in ERP/SAP teams
  • Post on niche forums (e.g., Data Vault Alliance, dbt Slack)
  • Offer remote or hybrid work to attract national talent if local market is tight

5. Metadata & Catalog Specialist

• Level: Mid-Level
• Skills: Experience with data catalog tools (Collibra, Alation), metadata standards, data lineage
• Recruiting Strategy:
  • Upskill a current data analyst or IT librarian with vendor training
  • Look for professionals in library sciences or technical documentation with interest in data
  • Internships with MIDS/MLIS programs

6. Data Quality Analyst

• Level: Junior to Mid-Level
• Skills: SQL, data validation, anomaly detection, familiarity with Talend, Informatica DQ
• Recruiting Strategy:
  • Hire from local bootcamps (e.g., DataCamp, General Assembly)
  • Offer paid internships tied to school projects (QA for automotive telemetry, service data)

7. DataOps / Platform Engineer

• Level: Mid-Level
• Skills: Python, CI/CD for data pipelines, dbt, Airflow, cloud platforms
• Recruiting Strategy:
  • Internal transfer from DevOps or platform teams
  • Sponsor capstone projects at universities focused on cloud-native data platform design
  • Offer flexible hours or remote options to attract top engineering talent affordably

8. Data Privacy & Compliance Officer

• Level: Shared Resource (Part-Time)
• Skills: GDPR, CCPA, ISO 27001, automotive-specific standards (e.g., NHTSA data reporting)
• Recruiting Strategy:
  • Collaborate with Legal or HR to share an existing compliance role
  • Hire part-time privacy consultant or external law firm for policy design

University & Intern Strategy

• Partner Schools: MIT, Stanford, Georgia Tech, University of Michigan, Carnegie Mellon, and local and state universities (Vanderbilt, MTSU, Belmont, UT, ...)
• Programs to Target:
  • Data Science & Analytics
  • Computer Science
  • Business Information Systems
  • Library/Information Sciences
• Tactics:
  • Sponsor senior projects (e.g., metadata cataloging for parts inventory)
  • Host data hackathons with automotive use cases
  • Provide summer internships and offer conversion to full-time after graduation
  • Tap alumni networks and career services offices

Training & Upskilling

• Work with HR to setp up clear career paths for each main roles: Data Analysts, Data Engineers, Data Scientists, Data Stewards, Project Managers, ...
• Ensure sufficient budget for annual upskilling sessions (onsite or offsite).

Execution Plan (12–18 Months)

Month	Activity
0–2	Hire/appoint CDO and Governance Lead
2–4	Define JD templates, career paths, internship program
4–6	Launch university partnerships & start recruiting stewards/interns
6–12	Fill specialist roles (architect, quality, metadata, platform)
12–18	Rotate interns into junior full-time positions; conduct skills audits & optimize team composition

Estimated Cost (Year 1)

Role Type	Count	Cost per Role	Total
Senior (CDO, Architect, Governance)	3	$170K–$350K	~$700K
Mid-Level (DQ, Metadata, Platform)	4	$130K–$160K	~$550K
Entry-Level (Stewards, Interns)	5	$70K–$100K	~$400K
Part-Time Compliance/Legal	1	$60K–$80K	~$70K
Total	13	–	~$1.7M–$1.9M/year

Risks & Remedies

Risks:

• Difficulty attracting and retaining data talent in a competitive market
• Underuse of internal staff due to unclear career paths
• Low data literacy among non-technical stakeholders

Remedies:

• Partner with universities for internships and joint projects
• Establish career ladders and mentorship for every data role
• Launch data literacy campaigns and internal bootcamps