Data & AI Engineers Jobs
Engineers who build the data foundations behind AI automation — pipelines, platforms and ML-ready data.
Key Data & AI Engineers Capabilities
The skills and strengths employers look for in this field.
Pipeline & ETL/ELT Engineering
Designing, building and maintaining batch and streaming data pipelines using tools such as Apache Airflow, dbt, Spark and Kafka to move and transform data reliably at scale.
SQL & Data Modelling
Advanced SQL plus dimensional and analytics-engineering modelling practices to produce clean, well-structured datasets that are trustworthy for analytics and AI.
Programming
Strong Python (and often Scala or Java) for data processing, automation scripting and integrating data systems with APIs and orchestration frameworks.
Cloud Data Platforms
Hands-on experience with AWS, Azure or GCP data services and warehouses/lakehouses such as Snowflake, BigQuery, Redshift and Databricks.
Big Data & Distributed Systems
Working with distributed processing frameworks and large-scale storage to handle high-volume, high-velocity data workloads efficiently.
DataOps & Automation
Applying CI/CD, infrastructure-as-code, testing, monitoring and observability to automate and harden data pipelines for production reliability.
ML & AI Data Enablement
Building feature pipelines, training datasets and serving layers that support machine learning and AI automation use cases.
Data Quality & Governance
Implementing validation, lineage, cataloguing and security/compliance controls to keep data accurate, discoverable and well-governed.
Data & AI Engineers Market Overview
Data and AI engineers build and operate the systems that move, transform and serve data across an organisation. They sit upstream of analytics, machine learning and AI automation work — without reliable pipelines and well-modelled data, downstream AI initiatives stall. Typical responsibilities include designing ETL/ELT pipelines, building cloud data platforms, implementing data quality and governance controls, and preparing clean, well-structured datasets for analysts and ML teams.
Demand in the US remains strong. Data engineering is consistently cited as one of the fastest-growing roles in technology, driven by the volume of data organisations now generate and the push to operationalise AI. Industry sources place the average US data engineer salary in the region of $123,000 to $137,000, with entry-level roles around $80,000 and senior or big data engineers commonly reaching $140,000 to $170,000 or more.
Within this category, titles increasingly overlap. Analytics engineers focus on transforming and modelling data (often with tools like dbt) for business consumption; ML and AI data engineers specialise in feature pipelines and data for model training and inference; and DataOps and data platform engineers emphasise reliability, automation, observability and CI/CD for data systems. For AI automation projects specifically, employers value engineers who can stand up scalable, governed and largely self-maintaining data pipelines.
Data & AI Engineers Salary Guide
Indicative ranges — actual pay varies by location, experience and employer.
Indicative US base-salary ranges based on aggregated 2024-2026 market data (Glassdoor, Indeed, ZipRecruiter, Salary.com, Coursera). Total compensation can be higher with bonuses, equity and benefits. Pay varies significantly by location, industry and company size, with major tech hubs (CA, NY, MA, WA, DC) at the upper end. Contract day rates are approximate and vary by engagement.
Live market data (1 role with salary on the board)
Data & AI Engineers Job Roles
Common job titles and roles for Data & AI Engineers professionals.
Professional Bodies & Qualifications
AWS Certified Data Engineer – Associate
Validates skills in building and operating data pipelines and analytics solutions on Amazon Web Services.
Microsoft Certified: Azure Data Engineer Associate
Demonstrates ability to design and implement data storage, processing and security on Microsoft Azure (Fabric pathway for newer exams).
Google Cloud Professional Data Engineer
Certifies design and operation of data processing systems and ML-enabling pipelines on Google Cloud Platform.
Databricks Certified Data Engineer (Associate/Professional)
Validates building and maintaining data pipelines and lakehouse solutions using the Databricks platform and Apache Spark.
SnowPro Core / Advanced Data Engineer (Snowflake)
Confirms expertise in data engineering and pipeline development on the Snowflake cloud data platform.
dbt Analytics Engineering Certification
Recognises proficiency in analytics-engineering data transformation and modelling workflows using dbt.
Bachelor's degree in Computer Science or related field
A common baseline requirement; many employers strongly prefer relevant internship or hands-on project experience over formal credentials alone.
Career Path & Progression
Junior / Entry-Level Data Engineer
Supports senior engineers with pipeline development and maintenance, basic data modelling and routine data management tasks while building core SQL, Python and cloud skills.
Data Engineer
Independently designs and implements scalable pipelines and data models, owns features end to end, and works across cloud data platforms and orchestration tooling.
Senior Data Engineer
Leads complex projects, makes architectural decisions, optimises performance and reliability at scale, and mentors junior engineers.
Lead / Principal / Platform Engineer
Sets data platform strategy and standards, drives DataOps and automation practices org-wide, and influences how AI and analytics initiatives are enabled.
Data Engineering Manager / Architect
Manages teams or owns enterprise data architecture, aligning data infrastructure investment with business and AI objectives.