Key Responsibilities and Required Skills for Data Migration Specialist

🎯 Role Definition

A Data Migration Specialist is the technical lead and practitioner responsible for planning, designing, building, testing, executing and validating data migration projects. This role delivers repeatable, automated migration pipelines and runbooks that preserve data integrity, lineage, security and performance across source and target systems — including ERP, CRM, data warehouses, data lakes and cloud platforms. The specialist pairs deep SQL, ETL/ELT and scripting skills with experience using migration tools (Informatica, Talend, SSIS, AWS DMS, Azure Data Factory) to move and transform large-scale datasets while minimizing downtime and operational risk.

📈 Career Progression

Typical Career Path

Entry Point From:

ETL Developer / Data Engineer
Database Administrator (DBA)
Business Systems Analyst with migration project experience

Advancement To:

Senior Data Migration Architect
Data Platform Architect / Cloud Data Architect
Head of Data Migrations / Data Platform Manager

Lateral Moves:

Cloud Engineer (data migration focus)
Data Governance / Data Quality Lead
Integration Architect

Core Responsibilities

Primary Functions

Lead end-to-end data migration initiatives: define scope, create migration strategies, produce detailed migration design documents, and own delivery timelines for on-premise, cloud, and hybrid projects.
Assess source and target systems by performing comprehensive data profiling, gap analysis, metadata extraction, and source-to-target mapping to identify data quality issues, format discrepancies and transformation requirements.
Design robust ETL/ELT pipelines and data transformation logic using industry tools (Informatica, Talend, SSIS, AWS Glue, Azure Data Factory) and custom scripts (Python, SQL, Shell) to handle full loads, incremental loads and change data capture (CDC).
Develop and maintain detailed data mapping specifications, business rules, and transformation matrices that ensure semantic alignment between legacy schemas and modern target models (star schema, normalized models, cloud-native formats).
Build automated, repeatable migration frameworks and templates including parameterized jobs, orchestration (Airflow, Azure Data Factory, Control-M), logging, alerting and retry logic to scale migrations across multiple systems.
Implement and validate CDC solutions (Debezium, AWS DMS, SQL Server CDC) and near real-time replication strategies to minimize downtime and maintain transactional consistency during cutover windows.
Author and execute comprehensive data validation and reconciliation plans — automated unit tests, reconciliation scripts, row counts, checksums, referential integrity checks, and business-acceptance testing to certify migration accuracy.
Create and execute cutover plans and rollback strategies in collaboration with application owners, DBAs and infrastructure teams to ensure controlled switchover with minimal business disruption.
Perform performance tuning and optimization of migration jobs, SQL queries and target load processes to meet SLA constraints and reduce overall migration time.
Manage large-volume data conversions including transformation of legacy encodings, date formats, data normalization, deduplication, and address standardization while preserving lineage and auditability.
Secure sensitive data in transit and at rest during the migration lifecycle: implement encryption, tokenization, masking and anonymization techniques to meet GDPR, HIPAA, PCI and internal compliance requirements.
Coordinate with cross-functional stakeholders (application teams, product owners, network, security, QA) to document dependencies, test plans, and deployment windows for migration activities.
Maintain metadata and data lineage by updating catalogs (Collibra, Alation, AWS Glue Data Catalog) and ensuring accurate documentation for each migrated dataset and transformation step.
Develop and run comprehensive migration testing suites: unit tests, system integration tests, performance tests, and user acceptance tests with clear pass/fail criteria and traceability of fixes.
Operate migration monitoring and observability: configure metrics, dashboards (Grafana, CloudWatch, Azure Monitor), and alerts to track job health, throughput, latency, and error rates in real time.
Manage third-party and vendor tools and contractors engaged in migration projects: evaluate tool fit, run POCs, manage licenses, and ensure deliverables meet enterprise standards.
Produce runbooks, SOPs and training materials for operations teams to take over post-migration monitoring, support and incremental loads.
Implement CI/CD for migration code and pipelines using Git, branching strategies, automated testing and deployment pipelines to non-production and production environments.
Troubleshoot and resolve complex migration failures, investigate root causes, and implement permanent fixes to prevent recurrence while documenting lessons learned.
Estimate effort, resource requirements and risks for migration projects and maintain up-to-date project plans, risk registers and communication logs for executive reporting.
Ensure data governance practices are embedded in migration designs — data ownership, stewardship, retention policies, and access controls must be maintained and enforced during migration.
Convert legacy stored procedures, custom scripts and integration logic into maintainable, cloud-native solutions while preserving functional parity and business rules.
Provide post-migration support and stabilization: execute reconciliation, remediate defects, conduct performance tuning and ensure business users sign off on migrated datasets.

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 engineers and build team capability around migration best practices and tooling.
Maintain best-practice documentation on versioning, schema evolution and backward compatibility strategies.
Participate in vendor evaluations, proof-of-concepts and migration tool bench testing to recommend cost-effective, scalable solutions.
Provide estimations and scope breakdowns to PMO and help prioritize migration backlogs based on business impact.

Required Skills & Competencies

Hard Skills (Technical)

Advanced SQL expertise (Oracle, SQL Server, PostgreSQL, MySQL) for complex data transformations, reconciliation and performance tuning.
Hands-on experience with ETL/ELT tools and platforms: Informatica PowerCenter/Cloud, Talend, SSIS, Matillion, AWS Glue, Azure Data Factory.
Cloud migration experience across AWS, Azure or GCP — familiarity with services like AWS DMS, S3, Redshift, RDS, Azure Data Factory, Azure SQL, BigQuery.
Scripting and automation proficiency: Python, Bash/Shell, PowerShell for data manipulation, orchestration and custom connectors.
Experience implementing Change Data Capture (CDC) and real-time replication patterns (Debezium, GoldenGate, AWS DMS).
Data profiling, cleansing and data quality tool experience (Ataccama, Talend Data Quality, Great Expectations) and writing automated validation tests.
Strong understanding of data modeling, normalization, denormalization, schema design and star/snowflake dimensional models.
Familiarity with APIs, JSON, XML, RESTful integrations, web services and bulk data transfer methods for SaaS migrations (Salesforce, Workday).
Experience with large dataset handling: partitioning strategies, parallel processing, batching, and bulk load utilities (bcp, COPY).
Version control and CI/CD tooling: Git, Jenkins/GitHub Actions, Azure DevOps to manage migration code and deployments.
Observability and monitoring tooling: CloudWatch, Azure Monitor, Grafana, Prometheus for job and pipeline health.
Metadata and catalog management experience: Alation, Collibra, AWS Glue Data Catalog or similar.
Knowledge of security, encryption and data privacy: TLS, KMS, tokenization, GDPR/HIPAA/PCI compliance controls.
Proven ability with database administration tasks: backup/restore strategies, indexing, execution plans and storage considerations.

Soft Skills

Strong stakeholder management and communication skills to coordinate across business, QA, operations and leadership.
Analytical problem-solving and root cause analysis under time-sensitive migration windows.
Meticulous attention to detail and a focus on data accuracy, reconciliation and auditability.
Project planning and prioritization: ability to estimate effort, balance trade-offs and manage multiple simultaneous migrations.
Collaborative mindset: works effectively in cross-functional and agile teams.
Teaching and mentoring skills to uplift junior staff and create knowledge transfer documentation.
Resilience and adaptability when responding to production incidents and tight cutover deadlines.
Customer-centric orientation with the ability to translate technical details into business-impact statements.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in Computer Science, Information Systems, Data Engineering, Software Engineering, or equivalent practical experience (4+ years) in data migration or ETL roles.

Preferred Education:

Master’s degree in Data Science, Computer Science, or MBA with technical concentration; or relevant certifications (AWS Certified Data Analytics, Google Professional Data Engineer, Microsoft Azure Data Engineer, Informatica/Talend certifications).

Relevant Fields of Study:

Computer Science / Software Engineering
Data Engineering / Information Systems
Applied Mathematics / Statistics
Business Information Systems

Experience Requirements

Typical Experience Range: 3–8 years of progressive experience in data migration, ETL/ELT development and database systems.

Preferred: 5+ years with multiple end-to-end enterprise migration projects, demonstrable experience migrating OLTP and OLAP systems to cloud data platforms, hands-on tool experience (Informatica, Talend, SSIS, AWS DMS, Azure Data Factory) and history of delivering low-downtime cutovers and validated reconciliations for mission-critical datasets.