Key Responsibilities and Required Skills for Data Conversion Consultant

🎯 Role Definition

A Data Conversion Consultant is a specialist who plans, designs, and executes data migration and conversion projects to move, transform, and validate enterprise data between systems (legacy-to-cloud, ERP/CRM upgrades, SaaS implementations). This role requires deep technical know-how in ETL and data transformation, a methodical approach to data quality and reconciliation, and strong communication skills to work with business stakeholders, data stewards, and technical teams to ensure accurate, auditable, and timely conversions.

Key search-friendly responsibilities and competencies include: data migration strategy, data mapping, ETL development, data profiling, data cleansing, reconciliation, cutover planning, validation testing, legacy data extraction, API and file-based integrations, metadata management, data governance, and stakeholder management.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Analyst specializing in data quality, profiling and SQL-based transformations.
ETL Developer or BI Developer with experience in mapping and integration tools.
Business Systems Analyst with strong domain knowledge of ERP/CRM systems and data flows.

Advancement To:

Senior Data Conversion Consultant / Lead Data Migration Engineer
Data Migration Manager or Program Manager (overseeing multiple migration streams)
Data Architect or Enterprise Data Lead focused on master data and integration strategy

Lateral Moves:

Data Quality Manager
Integration Architect
Business Analyst (Application/ERP focus)

Core Responsibilities

Primary Functions

Lead end-to-end data conversion efforts for medium to large enterprise projects, including initial discovery, data profiling, mapping, transformation, testing, go-live cutover and post-migration reconciliation to ensure data integrity and business continuity.
Perform comprehensive data profiling and analysis of source systems to quantify data quality issues, document anomalies, and propose remediation plans prior to conversion.
Develop detailed data mapping specifications that translate business requirements into technical ETL designs, mapping source fields to target schemas with transformation rules, lookup logic, and business validations.
Design, build and optimize ETL processes using industry tools (e.g., Informatica, Talend, SSIS) or custom scripts (Python, SQL, Bash) to extract, transform and load large volumes of structured and semi-structured data.
Implement robust data cleansing routines to standardize addresses, names, codes, and reference data, including deduplication, normalization and enrichment workflows to meet target system standards.
Create and execute test plans for conversion validation (unit, system, integration and user acceptance testing), develop test cases and control tables, and orchestrate data validation cycles with business users.
Develop reconciliation reports and automated validation scripts that compare source and target datasets, highlight discrepancies, and support root cause analysis and remediation tracking.
Collaborate with business SMEs and data owners to define business rules, mapping exceptions and acceptable data thresholds; obtain sign-off on mapping documents and validation criteria.
Lead legacy data extraction activities across databases, flat files, mainframes, and API endpoints, coordinating with platform teams to schedule extracts and ensure secure, performant data transfers.
Manage cutover planning and execution, including dry-run migrations, rollback procedures, downtime estimation, communication plans, and post-cutover verification to minimize operational disruption.
Configure and maintain metadata and lineage documentation, capturing transformation logic, mapping history and data stewardship notes to support auditability and future migrations.
Provide technical leadership to junior developers and consultants on conversion best practices, coding standards, error handling and performance tuning techniques.
Develop automation around repetitive conversion tasks—such as automated mapping generation helpers, data quality scoring, and deployment scripts—to increase repeatability and reduce manual errors.
Integrate conversions into CI/CD pipelines and release management processes to ensure consistent deployments across development, QA, UAT and production environments.
Build and maintain connectivity to cloud storage and services (AWS S3, Azure Blob, Google Cloud Storage) and leverage cloud-native ETL and data transformation services where appropriate.
Handle complex data model transformations, including normalization/denormalization, historical data migration (slowly changing dimensions), and preservation of audit trails and effective dating.
Ensure compliance with data security and privacy policies during extraction, transport and storage (encryption, access controls, anonymization/pseudonymization where required).
Troubleshoot conversion failures in real time during cutovers, perform root cause analysis and implement corrective actions to meet go-live deadlines.
Partner with QA, Release Management and Operations teams to ensure conversion artifacts are properly version-controlled, tested and deployed according to the project schedule.
Produce clear, stakeholder-focused documentation and status reports for project governance: mapping sign-offs, issue logs, acceptance criteria, migration runbooks, and post-migration retrospectives.
Support data governance initiatives by collaborating with MDM and data stewardship teams to align conversion outputs to master data definitions, taxonomies and glossaries.
Coordinate cross-functional workshops (data discovery, mapping, reconciliation walkthroughs) to socialize conversion approaches and accelerate decision-making.
Estimate effort and resource requirements for data conversion workstreams, contribute to project planning, and track actuals against estimates to support project financials.
Provide post-migration support and knowledge transfer to operational teams and system administrators, including training on reconciliation tools, runbooks, and escalation paths.
Adapt conversion plans to accommodate iterative/agile delivery models, working in sprints to deliver incremental datasets that support phased deployments and parallel testing.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Mentor junior data engineers and conversion specialists on techniques for data profiling, mapping and testing.
Help evaluate and onboard new data conversion tools and vendor solutions, including proof-of-concept testing and ROI assessments.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL: proficient in writing complex queries, window functions, joins, CTEs and performance tuning for large datasets.
ETL Tools: hands-on experience with enterprise ETL/integration platforms such as Informatica PowerCenter, Talend, SSIS, Matillion or Fivetran.
Scripting & Automation: practical experience with Python, shell scripting, or PowerShell for data extraction, transformation, automation and reconciliation tasks.
Data Modeling: strong understanding of relational and dimensional data modeling, entity-relationship diagrams, and mapping complex source-to-target relationships.
Data Profiling & Quality Tools: experience using profiling tools (e.g., Talend Data Preparation, Trifacta, SQL-based profiling) and designing data quality rules.
Data Formats & APIs: familiarity with CSV, JSON, XML, Avro, Parquet, FTP/SFTP, RESTful APIs and working with file-based and API-based extracts.
Database Platforms: experience with major RDBMS (SQL Server, Oracle, PostgreSQL, MySQL) and working knowledge of NoSQL or big data stores as applicable.
Cloud Platforms & Services: hands-on exposure to AWS, Azure or Google Cloud for storage, compute and managed ETL services (Glue, Data Factory, Dataflow).
Version Control & CI/CD: practical use of Git, CI/CD pipelines and deployment practices for data artifacts and conversion scripts.
Data Governance & Security: knowledge of data governance practices, GDPR/CCPA considerations, encryption, masking and secure data handling during migrations.
Testing & Validation: proficiency in developing test cases, automated reconciliation scripts and regression testing for data accuracy and completeness.
ERP/CRM Domain Knowledge: prior experience migrating data for ERP systems (SAP, Oracle EBS) or SaaS platforms (Salesforce, Workday) is highly desirable.
Performance Tuning & Optimization: ability to analyze and optimize ETL jobs, SQL, and transformation logic to meet SLAs and cutover windows.
Reporting & Visualization: ability to produce reconciliation, exception and status reports using Excel, SQL, or BI tools (Power BI, Tableau) to communicate results to stakeholders.
Metadata & Lineage Management: experience documenting and maintaining metadata, data lineage and transformation logic to support auditability.

(Include at least 10 of the above: SQL, Informatica/Talend/SSIS, Python/scripting, data modeling, data profiling, JSON/XML/APIs, RDBMS, cloud services, Git/CI-CD, data governance, testing/validation.)

Soft Skills

Strong stakeholder management: influence and negotiate mapping, acceptance criteria and cutover decisions with business owners and IT leadership.
Excellent communication: translate technical conversion details into clear, business-friendly updates and runbooks.
Analytical problem solving: investigate complex data mismatches and design pragmatic, repeatable remediation approaches.
Attention to detail: meticulous focus on field-level mapping, edge cases, and reconciliation to ensure accurate deliverables.
Project delivery mindset: prioritize tasks, manage timelines, and maintain quality during high-pressure go-live windows.
Adaptability: comfortable working across multiple systems, data formats and evolving project requirements.
Team leadership and mentoring: coach junior team members, review technical designs and enforce standards.
Customer-focused orientation: deliver conversion outputs that meet business validation criteria and end-user expectations.
Time management and organization: handle parallel migration streams and multiple stakeholder requests effectively.
Documentation and knowledge transfer skills: prepare comprehensive runbooks, mapping documents and training materials.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, or a related field; or equivalent practical experience in data migration and ETL.

Preferred Education:

Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, Business Analytics, or related technical discipline.
Certifications in data integration or cloud platforms (e.g., Informatica, Talend, AWS/GCP/Azure certifications) are a plus.

Relevant Fields of Study:

Computer Science
Information Systems
Data Engineering / Data Science
Software Engineering
Business Analytics

Experience Requirements

Typical Experience Range: 3–8 years of hands-on experience in data conversion, ETL development, or data migration projects.

Preferred: 5+ years with demonstrated experience leading multiple enterprise-level migrations (ERP/CRM/cloud), proven track record of successful cutovers, and experience with at least one major ETL tool plus scripting and cloud integration.