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Key Responsibilities and Required Skills for Data Conversion Specialist

💰 $55,000 - $95,000

Information TechnologyData EngineeringOperations

🎯 Role Definition

A Data Conversion Specialist designs, builds, and executes reliable data migration and conversion processes that move legacy data into target systems with integrity, accuracy, and traceability. This role balances technical ETL development, robust data validation, stakeholder coordination, and production cutover support. The ideal candidate is detail-oriented, experienced with data mapping and transformation languages, and fluent in SQL, scripting and common ETL/data quality tooling.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Junior Data Analyst or Data Technician
  • ETL Developer / Integration Developer
  • Data Quality Analyst

Advancement To:

  • Senior Data Conversion Specialist / Lead Data Migration Engineer
  • Data Migration Manager or Data Engineering Manager
  • Master Data Management (MDM) Lead or Data Architect

Lateral Moves:

  • ETL Developer
  • Data Quality Engineer
  • Business Systems Analyst (implementation focus)

Core Responsibilities

Primary Functions

  • Lead end‑to‑end data conversion projects: develop conversion strategy, define scope, estimate effort, prepare timelines, and deliver against migration milestones, ensuring minimal production disruption.
  • Create detailed data mapping and transformation specifications that map source data fields, types and values to target system schemas, including field-level business rules and default value logic.
  • Design, build and maintain ETL/ELT processes and scripts (SQL, Python, SSIS, Talend, Informatica, or similar) to extract, transform and load data from multiple legacy systems and file formats into the target application.
  • Perform comprehensive data profiling and discovery across source systems to identify data quality issues, patterns, nulls, duplicates and anomalies that impact migration scope and logic.
  • Conduct robust data cleansing and normalization efforts (standardization, deduplication, value normalization, lookups) to ensure target system readiness and compliance with business rules.
  • Develop, maintain and version conversion code, mappings and schema definitions in source‑controlled repositories; apply code review practices and maintain clear change logs.
  • Build and execute conversion test plans and test cases — unit tests, system tests, reconciliation tests and user acceptance tests — to validate transformation logic and ensure parity between source and target.
  • Reconcile converted data to source system records using row counts, checksum/hash comparisons, sample validation and aggregated total checks to validate completeness and accuracy.
  • Troubleshoot conversion failures and errors, perform root cause analysis and implement corrective actions, reprocessing and revalidation as required before production cutover.
  • Automate repeatable conversion tasks and validation checks to increase repeatability, reduce manual intervention and accelerate go‑live readiness.
  • Prepare and present conversion status reports, defect logs, data quality dashboards and migration readiness metrics to stakeholders and project leadership.
  • Coordinate with business SMEs, application owners and infrastructure teams to resolve mapping ambiguities, obtain access to source systems and align on cutover windows.
  • Design and implement data rollback and recovery plans for conversion errors during cutover, including staged backups, restore testing and contingency procedures.
  • Validate and transform complex data structures such as hierarchies, parent/child relationships, multi-value fields, attachments, references and cross‑entity links ensuring referential integrity post‑migration.
  • Perform format conversions between CSV, XML, JSON, EDI, fixed‑width and relational data formats and ensure encoding, delimiter and schema consistency across conversion pipelines.
  • Implement and maintain data dictionaries, conversion documentation, lineage diagrams and audit trails to support traceability and sustainment for future migrations and compliance audits.
  • Enforce and implement data governance policies, privacy controls and PII handling conventions during extraction, staging and conversion processes.
  • Participate in cutover planning and execute final production conversions, monitoring conversion jobs, job logs and performance metrics and providing immediate post‑conversion support to resolve data discrepancies.
  • Conduct post‑migration validation and remedial cycles, working closely with business users to reconcile functional impact, close defects, and transition the data to business operations.
  • Estimate conversion resourcing needs, create detailed work breakdown structures for conversion activities and track conversion-specific project budgets and time‑to‑value.
  • Continuously improve conversion toolchains and processes, researching and recommending tools (profilers, ETL, data quality) and best practices to reduce time, cost and risk for future projects.
  • Ensure compliance with regulatory and contractual requirements for data handling and retention during conversion projects, including encryption and secure transfer of sensitive datasets.
  • Provide mentoring and technical guidance to junior conversion engineers and analysts, reviewing code, mapping logic and test evidence to raise team capability and consistency.

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.
  • Assist in vendor selection and evaluation for migration tooling and third‑party conversion service providers.
  • Train end users and operational teams on converted data structures and new data access practices.
  • Maintain conversion runbooks and operational run schedules for repeatable migrations and integrations.

Required Skills & Competencies

Hard Skills (Technical)

  • SQL (advanced queries, stored procedures, joins, window functions, temp tables) — used daily for profiling, transformation and reconciliation.
  • ETL tools and platforms: SSIS, Talend, Informatica PowerCenter, Azure Data Factory, or comparable ETL/ELT tooling.
  • Scripting and programming: Python (pandas), shell scripting, PowerShell, or VBScript for automation and complex transforms.
  • Data mapping and transformation design — experience authoring detailed mapping documents and transformation rules.
  • Data profiling and data quality tools (e.g., Trifacta, Talend Data Quality, Informatica Data Quality) and techniques for assessing data readiness.
  • Working knowledge of file formats and serialization: CSV, XML, JSON, fixed-width, EDI; ability to parse and generate these formats reliably.
  • Database systems: Microsoft SQL Server, Oracle, MySQL, PostgreSQL — installation, querying and performance troubleshooting for large datasets.
  • Experience with version control (Git), ticketing systems (Jira) and CI/CD pipelines for migration code deployment.
  • Familiarity with APIs, RESTful services and bulk data transfer mechanisms for migration into SaaS/cloud platforms.
  • Data validation, reconciliation methodologies and auditing techniques (checksums, hashing, reconciliation reports).
  • Regular expressions for parsing, cleansing and transforming text‑based source data.
  • Exposure to data governance, PII/PHI handling, encryption in transit and at rest, and compliance standards (GDPR, HIPAA as applicable).
  • Experience converting and migrating data for common enterprise applications (ERP, CRM, HRIS, Billing, Finance systems).
  • Strong Excel skills (VLOOKUP, pivot tables, Power Query) for mapping, reconciliation and manual validation tasks.

Soft Skills

  • Exceptional attention to detail and commitment to data accuracy.
  • Strong written and verbal communication — able to translate technical data issues into business terms for stakeholders.
  • Analytical problem solving and root cause analysis under production pressure.
  • Project and time management — ability to juggle multiple conversions and cutover deadlines simultaneously.
  • Collaborative team player with stakeholder management and negotiation experience.
  • Adaptability and continuous learning mindset to keep up with new tools and data formats.

Education & Experience

Educational Background

Minimum Education:

  • Associate degree or diploma in Computer Science, Information Systems, Data Management, or related technical field; or equivalent practical experience.

Preferred Education:

  • Bachelor's degree in Computer Science, Information Systems, Data Science, Software Engineering, Business Analytics, or related field.

Relevant Fields of Study:

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

Experience Requirements

Typical Experience Range: 2–6 years of hands‑on experience performing data migrations, ETL development and conversion testing in enterprise environments.

Preferred:

  • 4+ years of direct experience with complex data conversions and migrations, including at least one full production cutover.
  • Prior exposure to industry‑specific systems (ERP, CRM, HRIS) and cloud migrations (SaaS migrations, AWS/Azure/GCP data migrations).
  • Demonstrated experience implementing data quality controls, reconciliation frameworks and delivering auditable migration evidence.
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