Data Integration And Ingestion

Topic

Name: Data integration and ingestion

Why it matters for FDE roles: Customer systems rarely have clean, complete, perfectly shaped data. FDEs need to bring data into a usable workflow while preserving context, quality, and trust.

Plain-English Definition

Data integration connects data from different systems. Data ingestion is the process of importing, validating, transforming, and storing that data for use.

Where It Shows Up

• Job listing signal: data pipelines, ingestion, ETL, APIs, customer data, analytics, operational systems.
• Real customer scenario: A customer wants tickets, docs, and account metadata combined so an AI workflow can answer with context.

Core Concepts

• Source inventory: where data comes from and which system is authoritative.
• Schema mapping: how fields translate between systems.
• Normalization: making inconsistent values usable.
• Validation: checking required fields, formats, and constraints.
• Incremental sync: importing new or changed records without duplication.
• Lineage: knowing where a record came from.

Failure Modes

• Importing data without knowing the source of truth.
• Losing important metadata during transformation.
• Duplicate records from retries or repeated imports.
• Stale data presented as current.
• Permissions ignored during ingestion or retrieval.

Interview Language

One sentence I could say in an interview:

I try to make data ingestion boring and inspectable: source, schema, validation, dedupe, timestamps, permissions, and lineage all matter before the AI layer sees anything.

Relevant work experience for this topic.

Links / References

• API Integrations
• RAG And Knowledge Systems