Independent verification of data migrations in regulated environments. Source and target exports are recomputed independently and every difference is
Aktualisiert am 25.08.2026
Profil
Freiberufler / Selbstständiger
Remote-Arbeit
Verfügbar ab: 24.08.2026
Verfügbar zu: 50%
davon vor Ort: 100%
Datenmigration
Computer System Validation
Datenqualität
SQL
ETL
GxP
Data handling
Python
Data Validation
Datenvalidierung
Testmanagement
LIMS
Veeva Vault
TrackWise
Datenanalyse
Reporting
Czech
Muttersprache
English
fluent

Einsatzorte

Einsatzorte

Deutschland
möglich

Projekte

Projekte

8 months
2026-01 - now

Independent Data Verification

  • Built a verification toolchain in Python that reconciles source and target exports, classifies every difference by type and severity, and generates the controlled document and discrepancy log automatically.
  • Validated the toolchain against blind test data of 4,000 records in which defects were injected without the verification having any knowledge of them. It identified records absent from the target in a contiguous block, duplicate primary keys, truncation at the target column width, stripped leading zeros on identifiers, a changed date representation affecting part of the population, character encoding failures, and two columns transposed for one batch.
  • Designed the reporting to state its own limits: where two defects meet on one record the per-field counts are reported as a lower bound rather than a complete census, and the number of records carrying more than one finding is given explicitly.
  • Earlier work in the same field: structuring post-market complaint data for medical device manufacturers. Deduplication, device name normalisation so events can be counted by product, IMDRF Adverse Event Terminology codes proposed where derivable from free-text narrative, and every missing field flagged with the count of records it affects.
on request
Czech Republic (remote)

Position

Position

Independent Data Migration Verification

Kompetenzen

Kompetenzen

Top-Skills

Datenmigration Computer System Validation Datenqualität SQL ETL GxP Data handling Python Data Validation Datenvalidierung Testmanagement LIMS Veeva Vault TrackWise Datenanalyse Reporting

Produkte / Standards / Erfahrungen / Methoden

Profile
The candidate verifies that data has survived migration intact. This involves performing independent calculations based on the two files?the export from the source system and the corresponding export from the target system?and reporting any discrepancies. The verification process is implemented independently of the migration process. If the check were based on the same assumptions as the migration itself, there would be a risk of merely reproducing migration errors rather than uncovering them; this is precisely why the separation was established.

SERVICE
  • Input
    • Source system export and target system export, any tabular format. 
    • No access to the live system required.
  • Checks
    • Record count reconciliation source to target. 
    • Primary key integrity: missing, extra, duplicated. 
    • Field-level value comparison on matched records. 
    • Field completeness. 
    • Truncation on free-text fields. 
    • Character encoding corruption. 
    • Numeric coercion including leading zero loss. 
    • Date format drift. 
    • Null literal substitution. 
    • Column mapping transposition.
  • Output 
    • Controlled document: document number, revision, status, approval block, scope and limitations statement, findings as condition, criteria, cause, effect and recommendation with severity ratings, acceptance page. 
    • Machine-readable discrepancy log alongside.
  • Boundaries
    • No regulatory or clinical determinations. 
    • No patient-identifiable data required; work is performed on record counts, metadata and field values. 
    • Delivered white-labelled under the client quality system.

TECHNICAL
  • Data
    • Full FDA MAUDE annual datasets processed end to end: device, narrative and patient files joined across millions of rows, with encoding and delimiter variation handled explicitly.
  • Method 
    • Deterministic pipelines built to fail loudly rather than silently. Record count reconciliation at every stage, explicit fail states, part and merge logic for datasets exceeding a single processing pass, and anti-truncation handling.
  • Documents
    • ?Controlled document authoring to ISO 13485 clause 4.2.4 conventions. Document control block, revision history, findings structure, severity model, limitations statement, data dictionary, gap register, acceptance page.

REGULATORY KNOWLEDGE
GAMP 5 second edition. EU GMP Annex 11. 21 CFR Part 11. ISO 13485. EU MDR post-market surveillance and MDCG 2022-21. 21 CFR 820.198 complaint files. 21 CFR 803 medical device reporting. IMDRF Adverse Event Terminology Annexes A, E and F. QMSR transition effective February 2026.

Programmiersprachen

  • Python
  • SQL
  • JavaScript
  • Pandas for reconciliation at scale
  • ReportLab for controlled document generation

Einsatzorte

Einsatzorte

Deutschland
möglich

Projekte

Projekte

8 months
2026-01 - now

Independent Data Verification

  • Built a verification toolchain in Python that reconciles source and target exports, classifies every difference by type and severity, and generates the controlled document and discrepancy log automatically.
  • Validated the toolchain against blind test data of 4,000 records in which defects were injected without the verification having any knowledge of them. It identified records absent from the target in a contiguous block, duplicate primary keys, truncation at the target column width, stripped leading zeros on identifiers, a changed date representation affecting part of the population, character encoding failures, and two columns transposed for one batch.
  • Designed the reporting to state its own limits: where two defects meet on one record the per-field counts are reported as a lower bound rather than a complete census, and the number of records carrying more than one finding is given explicitly.
  • Earlier work in the same field: structuring post-market complaint data for medical device manufacturers. Deduplication, device name normalisation so events can be counted by product, IMDRF Adverse Event Terminology codes proposed where derivable from free-text narrative, and every missing field flagged with the count of records it affects.
on request
Czech Republic (remote)

Position

Position

Independent Data Migration Verification

Kompetenzen

Kompetenzen

Top-Skills

Datenmigration Computer System Validation Datenqualität SQL ETL GxP Data handling Python Data Validation Datenvalidierung Testmanagement LIMS Veeva Vault TrackWise Datenanalyse Reporting

Produkte / Standards / Erfahrungen / Methoden

Profile
The candidate verifies that data has survived migration intact. This involves performing independent calculations based on the two files?the export from the source system and the corresponding export from the target system?and reporting any discrepancies. The verification process is implemented independently of the migration process. If the check were based on the same assumptions as the migration itself, there would be a risk of merely reproducing migration errors rather than uncovering them; this is precisely why the separation was established.

SERVICE
  • Input
    • Source system export and target system export, any tabular format. 
    • No access to the live system required.
  • Checks
    • Record count reconciliation source to target. 
    • Primary key integrity: missing, extra, duplicated. 
    • Field-level value comparison on matched records. 
    • Field completeness. 
    • Truncation on free-text fields. 
    • Character encoding corruption. 
    • Numeric coercion including leading zero loss. 
    • Date format drift. 
    • Null literal substitution. 
    • Column mapping transposition.
  • Output 
    • Controlled document: document number, revision, status, approval block, scope and limitations statement, findings as condition, criteria, cause, effect and recommendation with severity ratings, acceptance page. 
    • Machine-readable discrepancy log alongside.
  • Boundaries
    • No regulatory or clinical determinations. 
    • No patient-identifiable data required; work is performed on record counts, metadata and field values. 
    • Delivered white-labelled under the client quality system.

TECHNICAL
  • Data
    • Full FDA MAUDE annual datasets processed end to end: device, narrative and patient files joined across millions of rows, with encoding and delimiter variation handled explicitly.
  • Method 
    • Deterministic pipelines built to fail loudly rather than silently. Record count reconciliation at every stage, explicit fail states, part and merge logic for datasets exceeding a single processing pass, and anti-truncation handling.
  • Documents
    • ?Controlled document authoring to ISO 13485 clause 4.2.4 conventions. Document control block, revision history, findings structure, severity model, limitations statement, data dictionary, gap register, acceptance page.

REGULATORY KNOWLEDGE
GAMP 5 second edition. EU GMP Annex 11. 21 CFR Part 11. ISO 13485. EU MDR post-market surveillance and MDCG 2022-21. 21 CFR 820.198 complaint files. 21 CFR 803 medical device reporting. IMDRF Adverse Event Terminology Annexes A, E and F. QMSR transition effective February 2026.

Programmiersprachen

  • Python
  • SQL
  • JavaScript
  • Pandas for reconciliation at scale
  • ReportLab for controlled document generation

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