🔗Data Integrator

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Also known as: Integration Engineer, ETL Developer

Professional who designs and operates data ingestion, transformation, and synchronization across systems and formats.

DESCRIPTION

A Data Integrator builds and maintains pipelines that move and transform data between applications, warehouses, and lakes. They design batch and streaming flows, implement CDC, and ensure schema evolution and data contracts. The role balances performance, reliability, and cost while enforcing governance, lineage, and quality checks. Collaboration with application teams, data modelers, and stewards is essential to align integrations with business needs.

RELATED TERMS

Data Integration Extract-Transform-Load ELT Change Data Capture API Integration Message Broker Pipelines Model Data Schema Evolution Data Contracts

COMPETING TERMS

Data Engineer

KNOWLEDGE AREAS

Data Integration & Interoperability Data Engineering Data Architecture

TARGET AUDIENCE

Data Engineer Data Architect Analytics Engineer DevOps Engineer Information Analyst

EXTERNAL SOURCES

• Wikipedia - Data integration
• Microsoft - Reference patterns for ingestion.
• Talend - Overview of data integration.
• Informatica - Glossary for data integration.

TOP QUESTIONS

What does a Data Integrator focus on?

Building and operating pipelines that ingest, transform, and deliver data reliably and securely between systems.

Category: Responsibilities

How is a Data Integrator different from a Data Engineer?

Integrators center on movement and interoperability; data engineers may own broader platform, modeling, and performance responsibilities.

Category: Comparison

What patterns does a Data Integrator use?

Batch ETL/ELT, CDC, streaming, pub/sub, API-based integration, and event-driven orchestration.

Category: Patterns

How do Data Integrators ensure data quality?

They implement validations, contracts, schema checks, profiling, and monitoring with alerting on anomalies.

Category: Quality

What tools are common for Data Integrators?

Orchestrators (Airflow/Prefect), CDC tools, integration platforms, cloud data services, and quality frameworks.

Category: Tooling

How do Data Integrators manage schema evolution?

Through contracts, backward compatibility, versioning, and controlled rollout with canaries and validation tests.

Category: Governance

What is a common mistake in integration projects?

Tight coupling to source schemas; abstractions and contracts reduce breakage and rework.

Category: Mistakes

How do they handle sensitive data?

Use masking, tokenization, encryption, and least-privilege access, with controls audited and automated.

Category: Security

How to start as a Data Integrator?

Learn ETL/ELT, APIs, messaging patterns, and pick a cloud stack; build small pipelines and add governance from day one.

Category: Getting started