I'm interviewing for a senior engineer position and would like to do some light interview prep. The team I'd be joining is migrating and modernizing a legacy application. That application is used by organizations around the world, so it is key that the migration is smooth and painless.

Responsibilities

• Support legacy data migration efforts
• Prepare and validate customer data for migration
• Execute and monitor data load activities
• Investigate and resolve migration related activities
• Implement fixes and enhancements to migration tooling and related application code
• Deliver minor user interface improvements to support migration workflows.
• Conduct code reviews and ensure code quality standards
• Solve complex technical problems and identify practical solutions
• Collaborate with stakeholders on migration priorities and outcomes

Qualifications

• 4+ years of software dev experience
• Python (Django): Strong experience building and maintaining server-side applications. Comfortable with Django management commands, ORM bulk operations, transactions, and service-layer patterns. Experience with the data migration domain (ETL, data mapping, checkpointed jobs) is a plus.
• Data migration and ETL: Hands-on experience moving structured data between systems. Familiarity with XML/CSV parsing, metadata schemas, controlled vocabularies↳ Controlled Vocabularies, and bulk database loads.↳ Django and ETL Experience with data collections, DAM, or library/archive metadata is strongly preferred.↳ Dam and libary/archive metadata
• PostgreSQL: Solid understanding of relational databases, JSON/JSONB fields, upserts, and debugging data integrity issues at scale.
  ↳ 2 branches

  Branches from here

  • Upsert Overhead and Gotchas
  • JSON/JSONB Fields for Metadata
• TypeScript / React: Working proficiency for minor UI updates in Next.js App router codebase. ↳ Next.js App routerComfortable reading and extending existing components; deep SSR/RSC expertise not required for this role.
• Material UI (MUI): Familiarity with MUI V6+ for small UI changes.
• RESTful APIs: Experience consuming and integrating with RES APIs; OpenAPI/Swagger-generated clients a plus. ↳ Generated REST ClientsUnderstanding of multi-tenant SaaS and content/metadata-heavy domains is a plus.
• AWS and distributed workflows: Working knowledge of S3 ECS/Fargate tasks and event-driven ↳ ECS and Fargateprocessing (S3 notifications, SQS, background jobs). Experience running batch workloads in cloud environments preferred.↳ AWS Batch Workloads
• Problem-solving: Strong analytical skills for debugging partial-failure batch jobs, correlating error logs with source data, and proposing pragmatic fixes under migration timelines.
• Communication: Clear written documentation of migration issues, fixes, and rerun steps for operators and engineers.
• AI-assisted development: Comfort using AI coding tools for investigation, test scaffolding, and routine fixes; judgment to validate outputs against reports and tests.

Prompt

Create a study and interview prep plan that will only take a few hours to go through. For reference I am a seasoned python/django developer and am currently working as a DevOps engineer on AWS backed systems.

Senior Engineer Migration Role: Focused Prep Plan (3–4 hours)

You already have the strongest foundations for this role: Python/Django plus AWS operations. Focus your time on translating that experience into data-migration-specific design, operational judgment, and clear interview narratives.

1. What the interviewers are likely evaluating

They probably do not need you to prove you can write Django models or deploy to AWS. They will be looking for whether you can safely operate in a context where:

• Source data is inconsistent, incomplete, or unexpectedly large.
• Migrations have partial failures and must be safely resumed.
• A bad transformation can affect thousands or millions of records.
• Customers need understandable status, error reports, and rerun procedures.
• Timelines matter, but correctness, auditability, and reversibility matter more.
• You can improve existing tooling pragmatically rather than proposing a rewrite.

Frame your answers around these principles:

1. Make work idempotent.
2. Validate before mutation.
3. Use transactions at the right scope.
4. Checkpoint and make retries safe.
5. Preserve provenance and produce actionable errors.
6. Separate transformation logic from I/O and orchestration.
7. Measure and observe every stage.
8. Prefer a safe, operable solution over an elegant but risky one.

Suggested Schedule

If you only have two hours, prioritize sections 1, 2, 3, and 5.

1. Migration Concepts and Vocabulary — 35 minutes

Core migration pipeline

A solid default model:

Extract → Profile → Validate → Transform → Stage → Load → Reconcile → Report

Extract

Read from source systems: CSV/XML exports, APIs, S3 objects, legacy database dumps.

Important concerns:

• Encoding and delimiter issues
• Large files / streaming parsing
• Source versioning
• Immutable source-file retention
• Recording file checksums and source identifiers

Profile

Understand the incoming data before loading it:

• Row counts
• Null rates
• Unique-value counts
• Date ranges
• Invalid controlled-vocabulary values
• Unexpected schema changes
• Duplicates and identifier collisions

A strong phrase:

I would treat source profiling as a first-class migration stage, not just an implementation detail. It lets us identify data-quality problems before we create partial customer state in the destination system.

Validate

Distinguish two validation categories:

• Structural validation: required columns, XML schema shape, parseability, data types, file format.
• Business validation: required metadata, allowed vocabularies, referential integrity, tenant ownership, identifier uniqueness.

Useful distinction:

• Fatal errors: invalid file shape, wrong tenant, incompatible schema version. Do not start the load.
• Record-level errors: missing optional metadata, invalid vocabulary value, malformed date. Quarantine/report the record while allowing valid records to continue, depending on agreed policy.

Transform

Mapping legacy values to new-system representations:

def transform_record(source: dict, mappings: MappingConfig) -> AssetInput:
    return AssetInput(
        external_id=source["legacy_id"],
        title=clean_text(source.get("title")),
        creator=normalize_creator(source.get("author")),
        resource_type=mappings.resource_types.get(source.get("type")),
        metadata=build_metadata(source),
    )

Good design traits:

• Pure, testable transformation functions
• Explicit mapping/version configuration
• Preserved original source values where needed
• Clear handling for unknown values
• No hidden database calls in parsing/transformation code unless necessary

Stage

For complex or high-volume work, load normalized source records into a staging table before loading production tables.

Benefits:

• Auditability
• Easier replay/reprocessing
• SQL-based reconciliation
• Separation of source parsing from target writes
• Ability to review failures without reparsing source data

Load

Use bulk operations carefully, while preserving a way to identify which source records correspond to target records.

Reconcile

A migration is not complete when the job says “success.” It is complete when expected results match actual results.

Examples:

Source records:             100,000
Valid after validation:      99,850
Loaded successfully:         99,820
Quarantined:                     30
Unexpected failures:              0

Also reconcile:

• Counts by collection / tenant / resource type
• File/object counts
• Relationships and child records
• Checksums where files are copied
• Metadata completeness
• Sampled visual/UI verification

Concepts to be able to define quickly