Migration / Data Consultant

Ryan Mahoney

Why this role is hard · Ryan Mahoney

Hiring a Migration Specialist is tougher than it seems because the job rewards quiet precision over flashy expertise. You need someone willing to sit through messy legacy logs and listen to stakeholder limits while still delivering clean mappings. I have watched candidates ace technical interviews but fall apart when asked to reconcile two conflicting source systems during a dry run. The real test arrives when a staging pipeline breaks and you need them to take ownership while the architecture team moves forward. We are looking for steady operators who treat data quality like a daily habit.

Core Evaluation

Critical questions for this role

The competency and attitude questions below are where the hiring decision is made. They run in the live interview rounds and are calibrated to the level selected above.

21 Competency Questions

1 of 21

  1. Discipline

    Data Architecture & Integration Engineering

  2. Job requirement

    API Integration & Endpoint Development

    Configures standard API connectors, validates endpoint responses, and troubleshoots basic authentication failures.

  3. Expected at Junior

    Basic working proficiency supports troubleshooting standard integrations and prepares the specialist for more complex endpoint development at higher levels.

Interview round: Hiring Manager Technical Deep Dive

You're setting up an API endpoint to sync ticket attachments, but the authentication keeps failing. How do you troubleshoot and resolve the issue?

Positive indicators

  • Checks credential scope and permissions first
  • Captures exact error responses for analysis
  • Documents troubleshooting steps before escalation

Negative indicators

  • Randomly regenerates tokens without checking logs
  • Ignores documented authentication requirements
  • Attempts to bypass auth checks entirely

11 Attitude Questions

1 of 11

Active Listening

The disciplined cognitive and affective practice of fully receiving, processing, and reflecting stakeholder input—both explicit and implicit—to accurately translate complex operational, technical, and emotional cues into precise data strategies. In high-stakes migration environments, it requires suspending premature analytical judgment, validating historical workflow constraints and psychological safety needs, synthesizing divergent perspectives, and confirming mutual understanding before committing to technical architectures or transition timelines.

Interview round: Recruiter Screen & Role Alignment

A legacy system SME gives you a high-level overview of how incident attachments were historically stored, but skips over naming conventions. What steps do you take next?

Positive indicators

  • Seeks concrete examples to validate verbal descriptions
  • Checks metadata and naming rules systematically
  • Documents gaps before configuring extraction logic

Negative indicators

  • Assumes default naming conventions apply
  • Proceeds without verifying attachment metadata
  • Overlooks storage structure nuances

Supporting Evaluation

How candidates earn the selection conversation

The goal is to reduce effort for everyone by collecting more useful signal before adding more interviews. Lightweight application prompts and structured screens help the panel focus live time on the candidates most likely to succeed.

Stage 1 · Application

Filter at the door

Runs the moment a candidate hits Submit. Disqualifying answers end the application; everything else is captured for review.

Knock-out Questions

1 of 2

Application Screen: Knock-out

Do you have direct hands-on experience architecting and executing migrations that involve sensitive PII or HR employee records under strict compliance frameworks?

Yes
Qualifies
No
Auto-decline

Video-Response Questions

1 of 3

Application Screen: Video Response

You are midway through a critical cutover window when a legacy dependency issue forces a rollback. How would you communicate the revised timeline and new trigger conditions to non-technical operations leads and executive sponsors who are expecting a go-live announcement?

Candidate experience

REC
0:42 / 2:00
1Record
2Review
3Submit

Response time

2 min

Format

Recorded video

Stage 2 · Resume Screening

Read the resume against fixed criteria

Reviewers score every application that clears the door against the same criteria. Stronger reviews advance to live interviews; weaker ones are archived without further screening.

Resume Review Criteria

8 criteria
Demonstrates experience analyzing legacy data structures using scripting and query tools to establish quality metrics that inform migration scope and disposition decisions.
Shows capability in building, validating, and maintaining import sets, transform maps, and coalesce keys for structured data loads into target platforms.
Experience extracting configuration item relationships and mapping service topology from legacy CMDBs to ensure accurate target platform discovery.
Ability to monitor pre-cutover parallel runs, identify performance bottlenecks, and resolve staging failures or data exceptions during hypercare windows.

Does the resume show relevant prior work experience?

Does the resume indicate required academic credentials, relevant certifications, or necessary training?

Does the cover letter or personal statement convey clear relevance and familiarity with the job?

Is the resume complete, well-organized, and free from formatting, spelling, and grammar mistakes?

Stage 3 · During Interviews

Where the hire is decided

Interview rounds use the competency and attitude questions outlined above, then add tests, work simulations, and presentations that reveal deeper evidence about how the candidate thinks and works.

Coding Test

1 of 2

Live Interview · Coding Test

Without AI

Implement the function to load a CSV, compare it against the provided target_schema, identify missing/null fields and type mismatches, and return a JSON report of discrepancies.

Complete the Python function to profile legacy data against a target schema. Handle missing values, date format normalization, and type coercion safely. Return a structured discrepancy report.

With AI

Use an AI assistant to generate a baseline profiling script. Critically review its output for edge cases, modify it to handle ServiceNow staging table constraints, and document your changes.

Generate a baseline profiling script using AI, then critically review it for edge cases (e.g., date formats, null handling, memory limits). Refactor to meet staging table constraints and document why the AI's initial approach needed adjustment.

Response time

20 min

Positive indicators

  • Robust null/empty handling
  • Safe type coercion with fallbacks
  • Clear discrepancy reporting structure
  • Efficient pandas usage without loading entire datasets unnecessarily
  • Identifies AI blind spots (e.g., silent type coercion, missing chunking)
  • Adds explicit validation gates and error logging
  • Explains trade-offs between AI-generated code and production readiness
  • Documents changes clearly for team review

Negative indicators

  • Crashes on missing columns
  • Ignores date/timezone normalization
  • Returns unstructured or ambiguous error lists
  • Loads massive files into memory without chunking
  • Pastes AI output without verification
  • Fails to address memory or timezone edge cases
  • Cannot articulate why modifications were necessary
  • Overcomplicates simple profiling with unnecessary AI features

Presentation Prompt

Walk us through your approach to conducting a legacy system data inventory and scoping assessment for a client with poorly documented databases. Discuss how you would identify critical tables, handle undocumented customizations, and establish baseline reconciliation metrics without manual guesswork. Slides are optional; you can talk through your reasoning step-by-step.

Format

approach-walkthrough · 20 min · ~2 hr prep

Audience

Hiring panel consisting of a Senior Migration Engineer, a Principal Architect, and a Delivery Manager

What to prepare

  • A brief outline of your scoping methodology
  • Examples of how you've handled undocumented legacy fields in past projects (sanitized if necessary)
  • A list of clarifying questions you would ask the client upfront

Deliverables

  • A verbal walkthrough of your discovery process
  • A discussion of your risk mitigation and boundary-setting approach

Ground rules

  • Use only work you are permitted to share; anonymize client names and sensitive data
  • Focus on your reasoning and process, not building a new artifact
  • You may reference past projects, but do not bring proprietary code or unredacted exports

Scoring anchors

Exceeds
Systematically frames the discovery problem, proactively surfaces hidden risks, asks targeted clarifying questions, and establishes clear, defensible boundaries for scope and stakeholder access.
Meets
Provides a logical walkthrough of inventory scoping, identifies key validation steps, acknowledges documentation gaps, and communicates boundaries reasonably.
Below
Lacks a structured approach to discovery, overlooks undocumented data risks, struggles to articulate scoping boundaries, or fails to address stakeholder alignment and communication.

Response time

20 min

Positive indicators

  • Asks high-information clarifying questions about legacy constraints and data ownership
  • Surfaces assumptions about undocumented schemas and proposes concrete validation steps
  • Demonstrates a structured approach to inventory scoping and milestone alignment
  • Clearly articulates boundaries around scope creep and ad-hoc data requests
  • Adapts communication style to bridge technical discovery with non-technical stakeholder needs

Negative indicators

  • Jumps directly to tool selection without framing the discovery problem
  • Ignores or dismisses the challenge of undocumented historical customizations
  • Proposes vague or unstructured scoping methods lacking clear milestones
  • Fails to address how they would handle conflicting stakeholder demands for data access
  • Uses excessive technical jargon without explaining validation checkpoints

Work Simulation Scenario

Scenario. You are brought in to scope the data inventory and migration readiness for a legacy on-premise ITSM platform being replaced by ServiceNow. The legacy system has 15 years of undocumented customizations, inconsistent field usage, and known data decay. Your goal is to construct a reliable discovery plan and baseline reconciliation report.

Problem to solve. Determine the scope, data quality risks, and extraction approach for the legacy system by interviewing the primary legacy system custodian.

Format

discovery-interview · 40 min · ~2 hr prep

Success criteria

  • Ask high-information clarifying questions about undocumented fields and custom workflows
  • Surface assumptions about data decay and historical dependencies early
  • Construct a structured discovery approach without guessing or freezing
  • Establish clear boundaries for the scoping phase and next steps

What to review beforehand

  • Legacy system architecture overview
  • Standard migration scoping checklist
  • ServiceNow data import best practices

Ground rules

  • You are driving a 40-minute discovery interview
  • Ask clarifying questions before proposing solutions
  • Document key findings and assumptions in real-time
  • Focus on your approach to discovery, not on producing a final deliverable

Roles in scenario

Legacy System Custodian (informed_partner, played by peer)

Motivation. Wants the migration to succeed but is protective of the legacy system's quirks and worried about being blamed for missing data or broken workflows.

Constraints

  • Limited access to original documentation
  • System is scheduled for decommissioning in 6 months
  • Cannot grant direct database read access until scoping is formally approved

Tensions to introduce

  • Mention 'special' legacy tables that hold critical business logic but are not documented
  • Hint at data inconsistencies from past vendor changes without volunteering specifics
  • Express anxiety about scope creep and expectations for manual cleanup

In-character guidance

  • Answer questions directly and honestly when asked
  • Provide concrete examples of legacy quirks if probed
  • Maintain a cooperative but slightly guarded tone

Do not

  • Do not volunteer information the candidate did not ask for
  • Do not steer the candidate toward a preferred scoping methodology
  • Do not solve the data mapping or extraction problem for the candidate

Scoring anchors

Exceeds
Systematically uncovers hidden dependencies, data decay risks, and extraction constraints through precise, layered questioning. Establishes a clear, defensible discovery roadmap with explicit boundaries.
Meets
Asks relevant clarifying questions, identifies major legacy system risks, and proposes a reasonable, structured discovery approach.
Below
Relies on assumptions, asks superficial questions, or struggles to structure the scoping conversation under ambiguity.

Response time

40 min

Positive indicators

  • Asks targeted questions about undocumented customizations and historical data decay
  • Surfaces assumptions about data ownership and extraction feasibility early
  • Structures the interview logically, moving from high-level scope to specific table dependencies
  • Establishes clear boundaries for the discovery phase and defines concrete next steps

Negative indicators

  • Guesses at legacy data structures or mapping logic without asking clarifying questions
  • Freezes or defaults to generic migration templates when faced with ambiguity
  • Overlooks data quality risks and focuses only on volume or velocity
  • Fails to establish scoping boundaries, allowing scope creep into execution tasks

Progression Framework

This table shows how competencies evolve across experience levels. Each cell shows competency at that level.

Data Architecture & Integration Engineering

5 competencies

CompetencyJuniorMidSenior
API Integration & Endpoint Development

Configures standard API connectors, validates endpoint responses, and troubleshoots basic authentication failures.

Develops custom integration endpoints, implements error-handling and retry logic, and secures data-in-transit.

Architects API ecosystems, defines integration patterns (sync/async/event-driven), and establishes enterprise API governance.

ETL Pipeline Design & Data Transformation

Builds and runs predefined ETL jobs, monitors execution logs, and resolves basic data mapping errors.

Designs scalable transformation pipelines, implements complex business rules, and optimizes job performance for large datasets.

Defines enterprise ETL architecture standards, evaluates emerging data orchestration technologies, and directs pipeline modernization initiatives.

Legacy System Discovery & Assessment

Conducts source system scans, documents data dictionaries, and maps basic entity relationships under supervision.

Leads discovery workshops, identifies complex cross-system dependencies, and defines extraction strategies for heterogeneous environments.

Architects enterprise-wide discovery frameworks, establishes data lineage standards, and aligns legacy assessments with long-term platform roadmaps.

Platform Configuration & Schema Mapping

Applies standard schema mappings, configures basic custom fields, and validates structural alignment against templates.

Resolves schema conflicts, designs custom data models for platform extensions, and ensures referential integrity across modules.

Governs platform data architecture, establishes schema governance policies, and designs multi-tenant or federated data strategies.

Unstructured Asset & File Migration

Executes bulk file transfers, verifies attachment counts, and organizes assets into standard folder structures.

Automates asset migration workflows, handles large-volume transfers with chunking, and preserves metadata fidelity.

Designs enterprise content migration strategies, integrates digital asset management systems, and optimizes storage cost models.

Migration Operations & Quality Governance

5 competencies

CompetencyJuniorMidSenior
Cutover Execution & Deployment Orchestration

Follows cutover runbooks, executes sequential deployment steps, and logs transition milestones.

Orchestrates complex parallel cutovers, manages rollback triggers, and coordinates cross-team execution windows.

Architects automated cutover frameworks, defines enterprise deployment SLAs, and directs large-scale transition command centers.

Operational Handover & Knowledge Transfer

Compiles standard operating procedures, conducts user training sessions, and distributes handover documentation.

Develops comprehensive knowledge bases, mentors support staff, and establishes feedback loops for operational readiness.

Architects enterprise knowledge management strategies, aligns handover processes with ITIL/service frameworks, and drives continuous learning cultures.

Post-Migration Optimization & Performance Tuning

Monitors dashboard alerts, identifies slow-running processes, and applies standard tuning recommendations.

Conducts root-cause analysis for performance bottlenecks, refines data models, and implements indexing strategies.

Defines performance engineering standards, architects capacity planning models, and leads continuous improvement initiatives.

Quality Assurance & Data Validation Testing

Runs predefined validation scripts, records discrepancies, and retests corrected data sets.

Develops comprehensive test strategies, automates reconciliation processes, and resolves complex data integrity defects.

Establishes enterprise quality benchmarks, integrates continuous validation into migration pipelines, and drives defect prevention methodologies.

Security Compliance & Data Privacy Controls

Applies baseline encryption, configures standard access roles, and runs compliance checklists.

Implements dynamic data masking, designs secure credential vault integrations, and conducts security gap analyses.

Defines enterprise security frameworks for data migration, aligns controls with regulatory mandates, and leads incident response planning.