Data Platform Engineer
SOC 15-1243.00 · ESCO 2120 · OSCA 223233
Role snapshot
Overview
Builds and maintains the infrastructure that allows data to flow reliably from source systems into warehouses and analytics tools. This involves designing, implementing, and optimizing data pipelines, managing data storage solutions, and ensuring that data teams have quick and accurate access to the data they need for analysis and machine learning.
Enables data-driven decision-making across an organization by providing robust, scalable, and accessible data infrastructure, directly supporting analytics, reporting, and AI initiatives.
On the job
- Design, build, and maintain scalable data pipelines using various ETL/ELT tools and programming languages.
- Manage and optimize data storage solutions, including data warehouses, data lakes, and streaming platforms.
- Monitor data quality, system performance, and troubleshoot data-related issues to ensure data integrity and availability.
- Collaborate with data scientists, analysts, and software engineers to understand their data needs and provide appropriate solutions.
- Implement and enforce data governance, security, and compliance policies within the data platform.
Tools & technology
Average salary
Job outlook
ExcellentNew job opportunities are highly likely. Demand significantly outpaces supply in most markets.
Education & training
A bachelor's degree in Computer Science, Data Science, Software Engineering, or a related technical field is typically required, though relevant certifications and extensive experience can sometimes substitute.
AI impact outlook
Note — this is our current view. AI is moving fast, so we revisit these ratings.
Show how this was assessed Hide the detail
Note — this is our current view. AI is moving fast, so we revisit these ratings.
Show how this was assessed Hide the detailWhy this role received this rating
Core task exposure
high
How much of the role’s important work could AI perform?
Building and maintaining data pipelines, optimizing storage, and monitoring performance are highly exposed to AI-driven code generation and automation.
End-to-end automation
moderate
Can AI complete the work without substantial human involvement?
Designing complex, resilient, and secure data architectures, along with integrating diverse systems, requires significant human oversight.
Adoption pressure
high
How likely are employers to introduce AI into this work?
The imperative for efficient, reliable, and scalable data infrastructure drives strong adoption of AI tools to automate platform management.
Human dependence
strong
How much does success depend on human judgement, relationships and accountability?
Strategic architectural design, complex troubleshooting of distributed systems, and implementing data governance policies demand human expertise and judgment.
Protective — a higher rating lowers the overall score.
Role adaptability
strong
How easily can the role evolve as AI takes on more tasks?
The rapid evolution of data technologies and tools necessitates high adaptability, with continuous learning and re-skilling.
Shown for context — not part of the score.
What AI may take on
These are the parts of the role most likely to be automated or significantly accelerated.
- Automate pipeline code generation using various ETL/ELT tools
- Optimize data storage solutions and query performance
- Monitor data quality and system performance for anomalies
- Troubleshoot routine data-related issues and alerts
- Generate infrastructure as code for common deployments
Where people remain essential
These parts continue to depend heavily on human judgement, relationships and accountability.
- Design scalable and resilient data architectures
- Integrate diverse and often legacy data sources
- Implement and enforce complex data governance and security policies
- Collaborate with data scientists and analysts on their needs
- Perform deep-dive debugging of distributed data systems
How the role may evolve
From manual pipeline construction to architecting resilient systems and optimizing data flow.
The role will evolve from hands-on coding of individual pipelines to designing overarching data ecosystems and strategically optimizing their performance, reliability, and security.
Strengthen your future fit
- Advanced architectural design for data platforms
- Expertise in distributed systems and cloud infrastructure
- Strong data governance and security knowledge
- Complex problem-solving and debugging skills
- Strategic collaboration with diverse technical teams
- Assessment horizon
- 3–7 years
- Confidence
- High
- Last reviewed
- August 2026
- Methodology
- v1.0
This assessment reflects current AI capabilities and expected adoption patterns. Actual impacts will vary by industry, employer and the way each role is performed.
Career pathways
WHERE YOU COULD GO
CURRENT ROLE
Data Platform Engineer
Data & AI
ADJACENT MOVES
STARTING POINTS
Who thrives here
Interest profile
conventional · CIE
Individuals who thrive on systematic problem-solving, enjoy investigating complex technical challenges, and are motivated by building efficient, reliable systems will find this role rewarding.
Personality characteristics
Systematic
Approaches complex data architecture and pipeline design with a logical, structured, and organized methodology.
Investigative
Enjoys diagnosing and solving intricate technical issues within data systems, often involving deep dives into logs and configurations.
Composed
Remains calm and effective when troubleshooting critical data outages or handling urgent system failures under pressure.
Collaborative
Works effectively with data scientists, analysts, and other engineers to understand needs and integrate solutions.
Methodical
Follows established processes and best practices for data governance, security, and platform maintenance to ensure reliability.
Best for
- Individuals who enjoy designing, building, and optimizing complex technical systems.
- Engineers who are passionate about data infrastructure, scalability, and performance.
- Those who find satisfaction in providing reliable data foundations for others to build upon.
Watch out for
- Requires a tolerance for occasional on-call duties and urgent troubleshooting, which can disrupt planned work.
- The role is less focused on direct social interaction or artistic creation, favoring technical execution and system building.
A week in the life
A representative working week for a Data Platform Engineer — where the deep work, meetings, and admin actually land.
Real people. Real results.
Thousands of people
can't be wrong.
Frequently asked questions about Data Platform Engineer roles
What does a Data Platform Engineer do?
A Data Platform Engineer builds and maintains the infrastructure that allows data to flow reliably from source systems into warehouses and analytics tools. This involves designing, implementing, and optimizing data pipelines, managing data storage solutions, and ensuring that data teams have quick and accurate access to the data they need for analysis and machine learning. Enables data-driven decision-making across an organization by providing robust, scalable, and accessible data infrastructure, directly supporting analytics, reporting, and AI initiatives.
How much does a Data Platform Engineer earn?
A Data Platform Engineer earns a median of $135,000 per year in the US, typically ranging from $100,000 to $180,000.
What qualifications do you need to become a Data Platform Engineer?
To become a Data Platform Engineer, a bachelor's degree in Computer Science, Data Science, Software Engineering, or a related technical field is typically required, though relevant certifications and extensive experience can sometimes substitute.
What personality suits a Data Platform Engineer?
Data Platform Engineer roles tend to suit people who are highly conscientious — precise, organised and strong on follow-through (Conscientiousness 85/100) and open and curious — drawn to variety, ideas and new approaches (Openness 72/100). The traits that matter most in the role are Systematic, Investigative, Composed and Collaborative. Approaches complex data architecture and pipeline design with a logical, structured, and organized methodology. On interests, Data Platform Engineer maps to a CIE Holland Code profile — individuals who thrive on systematic problem-solving, enjoy investigating complex technical challenges, and are motivated by building efficient, reliable systems will find this role rewarding.
Who does a Data Platform Engineer role suit?
A Data Platform Engineer role is usually a strong fit for these reasons. Strong Investigative and Conventional tendencies align with the systematic problem-solving and technical challenges of building data platforms. The role demands a high degree of conscientiousness for ensuring data reliability and system uptime. Significant deep work blocks allow for focused problem-solving and infrastructure development.
What are the downsides of being a Data Platform Engineer?
Data Platform Engineer roles come with trade-offs worth weighing up. Requires a tolerance for occasional on-call duties and urgent troubleshooting, which can disrupt planned work. The role is less focused on direct social interaction or artistic creation, favoring technical execution and system building.
What is the work environment like for a Data Platform Engineer?
Work as a Data Platform Engineer is mostly office-based with hybrid arrangements common, semi-structured — a mix of set processes and self-directed work, a moderate pace and medium exposure to clients or stakeholders. Around 80% of the week is focused deep work.
What skills do you need to be a Data Platform Engineer?
Core skills for a Data Platform Engineer include Data pipeline development, Cloud platforms (AWS/Azure/GCP), Database management, ETL/ELT processes and Data governance and security.
How do you become a Data Platform Engineer?
Common entry routes into Data Platform Engineer roles include Junior Data Engineer, Software Engineer and DevOps Engineer.
What career progression is there for a Data Platform Engineer?
From a Data Platform Engineer role, common next steps include Senior Data Platform Engineer, Lead Data Platform Engineer and Data Architect; lateral moves include DevOps Engineer and Machine Learning Engineer.
What is the job outlook for Data Platform Engineer roles?
The outlook for Data Platform Engineer roles is currently rated excellent. New job opportunities are highly likely. Demand significantly outpaces supply in most markets.
Will AI replace Data Platform Engineer roles?
Traitstack rates automation risk for Data Platform Engineer roles at 66 out of 100, which is strong. While AI automates many routine infrastructure and pipeline tasks, human architects are essential for designing resilient systems and strategic optimization. AI is most likely to take on automate pipeline code generation using various etl/elt tools, optimize data storage solutions and query performance and monitor data quality and system performance for anomalies. Design scalable and resilient data architectures, integrate diverse and often legacy data sources and implement and enforce complex data governance and security policies stay with people. From manual pipeline construction to architecting resilient systems and optimizing data flow. That score measures how much of the work could change, not the likelihood the job disappears. It is Traitstack's current view, revisited as AI capability moves.