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Data EngineerJob Description

Everything recruiters need to write, post, and fill a data engineer role—fast.

Job Description Sample

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Job Title: Data Engineer

Location: Remote/Hybrid/On-site (flexible)

Type: Full-time

About the Role:

We are seeking a talented Data Engineer to join our growing data team and help build scalable data infrastructure. The ideal candidate will design and implement data pipelines, optimize data flow and storage, and collaborate with data scientists to enable data-driven decision making. This role offers the opportunity to work with cutting-edge technologies while solving complex data challenges.

Key Responsibilities:

  • Design, build and maintain scalable data processing pipelines and ETL workflows
  • Develop and optimize data warehouse solutions and architectures
  • Implement data quality controls and monitoring systems
  • Create automated data collection and processing systems
  • Collaborate with data scientists to prepare data for analysis
  • Build APIs and services for data access
  • Document data infrastructure and maintain data governance standards
  • Troubleshoot data pipeline issues and performance bottlenecks

Perks:

  • Competitive salary and equity package
  • Comprehensive health, dental and vision benefits
  • Flexible PTO and remote work options
  • Professional development budget

Data Engineer Responsibilities

Hiring a data engineer? Here's what you can expect them to handle:

  • Design and implement end-to-end data pipeline architectures
  • Build and maintain ETL processes for data warehousing
  • Optimize database performance and query efficiency
  • Ensure data quality, accuracy and security
  • Create automated testing and monitoring systems
  • Develop APIs for data access and integration
  • Collaborate with cross-functional teams on data projects
  • Document technical specifications and processes
Data Engineer Job Description

Qualifications to Be a Data Engineer

Here's what a solid candidate typically brings to the table:

CheckmarkBachelor's degree in Computer Science, Engineering or related field
Checkmark3+ years experience in data engineering or similar role
CheckmarkStrong programming skills in Python, Java or Scala
CheckmarkExpert knowledge of SQL and database design
CheckmarkExperience with cloud platforms (AWS/Azure/GCP)

Data Engineer Prerequisites

Before you even think of hiring, make sure your candidates have:

CheckmarkUnderstanding of data warehousing concepts
CheckmarkKnowledge of distributed computing
CheckmarkFamiliarity with data modeling techniques
CheckmarkExperience with ETL tools and frameworks
CheckmarkStrong problem-solving abilities

Data Engineer Hard Skills

The “must-haves” on every recruiter's checklist:

CheckProgramming Languages: Python, Java, SQL
CheckBig Data: Hadoop, Spark, Kafka
CheckDatabases: PostgreSQL, MongoDB, Cassandra
CheckCloud Platforms: AWS, Azure, GCP
CheckETL Tools: Airflow, Luigi, dbt
CheckData Warehousing: Snowflake, Redshift
CheckVersion Control: Git, GitHub
CheckContainerization: Docker, Kubernetes

Data Engineer Soft Skills

Tech skills get them in the door—soft skills help them stick around.

CheckStrong analytical and problem-solving abilities
CheckExcellent written and verbal communication
CheckAbility to work independently and in teams
CheckStrong project management capabilities
CheckAttention to detail and organization
CheckAdaptability to new technologies
CheckCritical thinking and decision making
CheckStakeholder management

Related Sections

Job Description SampleResponsibilitiesQualificationsSkills RequiredData Engineer SalaryFAQ

Frequently Asked QuestionsAbout Data Engineer Hiring

Data engineers focus on building and maintaining data infrastructure, while data scientists analyze data to derive insights. Engineers ensure data is accessible and usable, while scientists perform statistical analysis and build models.

Use a combination of system design questions, coding exercises focused on data pipeline scenarios, and SQL problem-solving tasks. Have candidates explain their approach to real-world data challenges.

Cloud platform experience is increasingly critical as most modern data infrastructure is cloud-based. However, strong database fundamentals are essential for optimizing data models and query performance.

Look out for candidates who can't explain data modeling concepts, lack version control experience, or have no experience with automated testing. These indicate gaps in fundamental engineering practices.

Start with 2-3 engineers for every 5-7 data scientists/analysts. This ratio ensures adequate support for data infrastructure while allowing for redundancy and specialized focus areas.

Tools and Programs Data Engineer Use

Here's what their digital toolbox might look like:

ETL Frameworks

Apache Airflow•Luigi

Databases

PostgreSQL•MongoDB

Cloud Services

AWS•Azure

Analytics

Tableau•PowerBI

Version Control

Git•GitHub

CI/CD

Jenkins•CircleCI

Monitoring

Grafana•Prometheus

Orchestration

Kubernetes•Docker

Job Description Examples

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