The Short Version

A data engineer builds the infrastructure that makes data usable. They create pipelines that extract data from source systems, transform it into useful formats, and load it where analysts, scientists, and applications can access it.

If data architecture is the blueprint, data engineering is the construction. Architects decide what gets built. Engineers make it work.

Without data engineers, your data stays trapped in production databases, SaaS tools, and spreadsheets - inaccessible to the people who need it for decisions.

What a Data Engineer Actually Does

Day to day, the work involves:

Building Pipelines

Moving data from point A to point B reliably. This includes:

  • Extraction - Pulling data from databases, APIs, files, streaming sources
  • Transformation - Cleaning, validating, reshaping data for downstream use
  • Loading - Writing data to warehouses, lakes, or other destinations
  • Orchestration - Scheduling jobs, managing dependencies, handling failures

Maintaining Systems

Production data infrastructure requires constant attention:

  • Monitoring pipeline health and performance
  • Debugging failures and data quality issues
  • Scaling systems as data volumes grow
  • Upgrading tools and migrating between platforms

Enabling Consumers

Data engineers don’t just move data - they make it accessible:

  • Building tables and views analysts can query
  • Creating datasets for data science models
  • Documenting schemas and business logic
  • Optimizing query performance

How It Differs From Other Data Roles

Data Architect - Designs the overall system. Decides what technologies to use, how data should flow, what standards apply. Engineers implement those decisions.

Data Analyst - Explores data and creates reports. Relies on engineers to provide clean, accessible data.

Data Scientist - Builds models and runs experiments. Needs engineers to prepare training data and deploy models to production.

Analytics Engineer - Transforms data in the warehouse using SQL and tools like dbt. Bridges engineering and analytics.

ML Engineer - Deploys and maintains machine learning models. Works alongside data engineers on feature pipelines and model serving.

The lines blur in smaller teams. A data engineer at a startup might do analytics engineering, some data science, and handle infrastructure. At larger companies, these roles specialize.

The Data Engineer’s Toolkit

Languages

  • Python - The default for data pipelines. Used with libraries like Pandas, PySpark, and orchestration tools.
  • SQL - Still the most important skill. Used everywhere from extraction to transformation to analysis.
  • Scala/Java - Common in Spark-heavy environments and legacy systems.

Transformation Tools

  • dbt - SQL-based transformation, increasingly standard for analytics engineering
  • Spark - Distributed processing for large-scale data
  • Pandas - Python data manipulation for smaller datasets

Orchestration

  • Airflow - The most widely adopted workflow orchestrator
  • Dagster - Modern alternative with better testing and observability
  • Prefect - Cloud-native orchestration
  • dbt Cloud - Managed dbt with scheduling and monitoring

Data Platforms

  • Snowflake, BigQuery, Databricks, Redshift - Cloud data warehouses
  • Delta Lake, Iceberg - Open table formats for lakehouses
  • Kafka, Kinesis - Streaming data platforms
  • Fivetran, Airbyte - Managed data ingestion

Infrastructure

  • Docker, Kubernetes - Containerization and orchestration
  • Terraform - Infrastructure as code
  • AWS, GCP, Azure - Cloud platforms

Junior vs Senior Data Engineers

Junior Data Engineers

  • Write and maintain individual pipelines
  • Debug issues with guidance
  • Learn the codebase and tools
  • Execute on well-defined tasks

Mid-Level Data Engineers

  • Own entire data domains or products
  • Design solutions for new requirements
  • Mentor juniors
  • Participate in architecture decisions

Senior Data Engineers

  • Shape technical direction across teams
  • Make build-vs-buy decisions
  • Define standards and best practices
  • Unblock complex problems others can’t solve
  • Bridge engineering with business stakeholders

The jump from junior to senior isn’t about writing more code. It’s about seeing the system holistically and making decisions that affect the whole platform.

When You Need Data Engineers

You probably don’t need data engineers if:

  • Your data lives in one or two systems
  • Analysts can query sources directly
  • Data volumes are small and stable
  • Off-the-shelf tools handle your needs

You probably do need data engineers if:

  • Data comes from many disconnected sources
  • Analysts waste time cleaning data manually
  • Reports take days to update
  • Data quality issues erode trust
  • You’re building data products or ML applications

How Many?

A rough guideline:

Company StageData EngineersNotes
Early startup0-1Analysts often handle simple pipelines
Growth stage1-3First dedicated hire, builds foundation
Scaling3-8Team structure emerges
Enterprise10+Specialized sub-teams

These numbers vary wildly based on data complexity, product needs, and how much you rely on managed services.

Data Engineer vs Data Architect

This confusion comes up constantly, especially in hiring.

AspectData EngineerData Architect
FocusBuilding and maintainingDesigning and deciding
TimeframeSprints and quartersYears
OutputWorking codeDecisions and documentation
ScopeSpecific pipelines/systemsEntire data platform
Typical levelIC to senior ICSenior IC to leadership

Small companies often combine these roles. As you scale, they separate. The architect handles cross-team decisions and long-term direction while engineers focus on implementation.

Many senior data engineers evolve into architects. The path runs through increasingly broad technical ownership.

Hiring Data Engineers

What to Look For

Must-haves:

  • Strong SQL skills
  • Python proficiency
  • Understanding of data modeling
  • Debugging and problem-solving ability

Nice-to-haves:

  • Experience with your specific stack
  • Cloud platform familiarity
  • Streaming experience (if relevant)
  • Domain knowledge

Red Flags

  • Can’t explain trade-offs in past decisions
  • No interest in understanding business context
  • Only wants to work on “exciting” problems
  • Can’t discuss failures or mistakes

Common Mistakes

  • Hiring for tools instead of fundamentals
  • Over-weighting credentials vs. practical experience
  • Expecting one engineer to solve all data problems
  • Not giving engineers access to business stakeholders

Frequently Asked Questions

What does a data engineer do?
A data engineer builds the infrastructure that makes data usable. They create pipelines that extract data from source systems, transform it into useful formats, and load it where analysts, scientists, and applications can access it.
What is the difference between a data engineer and a data analyst?
Data engineers build and maintain data pipelines and infrastructure. Data analysts explore data and create reports. Engineers make data accessible; analysts use that data to answer business questions.
What skills does a data engineer need?
Must-haves: strong SQL skills, Python proficiency, understanding of data modeling, and debugging ability. Nice-to-haves: cloud platform experience, streaming knowledge, and domain expertise.
What is the difference between a data engineer and a data architect?
Data engineers focus on building and maintaining pipelines within sprints. Data architects focus on designing systems and making decisions that span years. Engineers implement; architects design. Many senior engineers evolve into architects.
How many data engineers does a company need?
It varies: early startups often have 0-1, growth stage companies need 1-3 to build foundations, scaling companies have 3-8 with team structure emerging, and enterprises have 10+ with specialized sub-teams.