Data Migration Engineer

Summary

The client is a fast-growing software company building a modern operating system for communications service providers that manages billing, operations, and automation. The Data Migration Engineer will own customer migrations end-to-end by exploring legacy systems, exporting and transforming data, executing validated production imports, resolving discrepancies, and improving migration tooling.

Responsibilities

• Explore legacy source systems — databases, flat files, reports, APIs — to understand their structure, quality, and relationships in the context of the target data model
• Export source data across a range of access methods, including SQL, APIs, manual extracts, and database backups
• Transform exported data into the platform's standard format by authoring declarative, version-controlled mapping files that encode agreed business rules
• Execute migrations end-to-end on the internal migration platform: load, validate against target schemas, reconcile, and import to production
• Build and reuse mappings, templates, and validation rules so each migration is faster and more repeatable than the last
• Contribute engineering improvements to the migration tooling — validation rules, transforms, mapping updates — and raise larger needs to the platform engineering team
• Diagnose and resolve data discrepancies during dry runs, UAT, and post-cutover
• Document mapping specs, transformation logic, and data anomalies so migrations are auditable and repeatable

Skills

• 3–5+ years in data engineering, data migration, ETL, or a similar hands-on data role
• Strong SQL and ETL fundamentals — treating extract → transform → load as a deliberate methodology: staging, repeatable and idempotent loads, full vs. incremental loads, and validation and reconciliation at every stage
• Hands-on data-manipulation fluency across SQL, Python, JSON, or similar — querying, profiling, reshaping data, and debugging unfamiliar source schemas
• Experience extracting data from varied sources: relational databases, APIs, flat files, and backups
• AWS experience — particularly Athena — and depth in SQL-based systems
• A background in high-volume, structured, or legacy data environments — for example banking, legal software, payroll, large CRMs, or enterprise SaaS with financial modules
• A proven ability to transform messy, poorly documented data into clean, structured output, with a solid grasp of data quality, validation, reconciliation, and cleansing
• Client-facing communication skills — comfortable leading technical conversations directly with customers
• Comfort in fast-paced, ambiguous environments with shifting source systems, and a bias toward reusability and automation over one-off scripts
• A Bachelor's degree in Computer Science, Information Systems, Data or Software Engineering, or a related field — or equivalent work experience
• Familiarity with TypeScript/JavaScript (Node)
• Familiarity with OSS/BSS, telecom, or billing/CRM data models (subscribers, services, billing accounts) — or a demonstrated ability to ramp quickly on unfamiliar domain models
• Comfort using AI-assisted tooling to accelerate data exploration, mapping, and validation
• Experience building or extending an internal data platform or migration tooling
• Exposure to cutover and go-live operations: delta loads, reconciliation, rollback
• Experience collaborating with a separate platform or product engineering team

Qualifications

Must Haves

• 3–5+ years in data engineering, data migration, ETL, or a similar hands-on data role
• Strong SQL and ETL fundamentals — treating extract → transform → load as a deliberate methodology: staging, repeatable and idempotent loads, full vs. incremental loads, and validation and reconciliation at every stage
• Hands-on data-manipulation fluency across SQL, Python, JSON, or similar — querying, profiling, reshaping data, and debugging unfamiliar source schemas
• Experience extracting data from varied sources: relational databases, APIs, flat files, and backups
• AWS experience — particularly Athena — and depth in SQL-based systems
• A background in high-volume, structured, or legacy data environments — for example banking, legal software, payroll, large CRMs, or enterprise SaaS with financial modules
• A proven ability to transform messy, poorly documented data into clean, structured output, with a solid grasp of data quality, validation, reconciliation, and cleansing
• Client-facing communication skills — comfortable leading technical conversations directly with customers
• Comfort in fast-paced, ambiguous environments with shifting source systems, and a bias toward reusability and automation over one-off scripts
• A Bachelor's degree in Computer Science, Information Systems, Data or Software Engineering, or a related field — or equivalent work experience
• Familiarity with TypeScript/JavaScript (Node)
• Familiarity with OSS/BSS, telecom, or billing/CRM data models (subscribers, services, billing accounts) — or a demonstrated ability to ramp quickly on unfamiliar domain models
• Comfort using AI-assisted tooling to accelerate data exploration, mapping, and validation
• Experience building or extending an internal data platform or migration tooling
• Exposure to cutover and go-live operations: delta loads, reconciliation, rollback
• Experience collaborating with a separate platform or product engineering team

Benefits

• A flexible 25-day vacation policy and flexible working hours.
• Stock options.
• Group insurance and telemedicine.
• A life spending account and retirement savings plan.
• 100% remote work, company-wide.