Data Platform Engineer Jobs
Data Platform Engineer jobs are open across fintech, healthtech, enterprise software, and media at every level from new-grad to principal and staff, with specializations in data infrastructure, pipeline architecture, and cloud data warehouse engineering. Find a role that fits from the openings below and apply directly.
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Career Area:
Technology, Digital and DataJob Description:
Your Work Shapes the World at Caterpillar Inc.
When you join Caterpillar, you're joining a global team who cares not just about the work we do – but also about each other. We are the makers, problem solvers, and future world builders who are creating stronger, more sustainable communities. We don't just talk about progress and innovation here – we make it happen, with our customers, where we work and live. Together, we are building a better world, so we can all enjoy living in it.
Help Build the Future of Caterpillar –
At Caterpillar, technology always has a purpose, which is to solve our customers’ toughest challenges. Through Cat Technology, we are solving problems by building the intelligence layer that connects machines, data, and people to make jobsites safer, more productive, and more sustainable. By combining deep domain expertise in physical systems with software, connectivity, autonomy, and AI, we deliver solutions that work in the real world—on real jobsites, at global scale.
You’ll build and deploy against one of the most unique data foundations—over 1.6 million connected assets generating real-world data daily. These data and platform capabilities are enabling the development of AI models, edge computing architectures, and software systems that scale across fleets, products, and industries. The result will be a new generation of machines that continuously learn, improve, and deliver performance at scale.
Be Part of What’s Next in Autonomous Construction Sites
Construction autonomy is one of the most complex challenges in applied AI, and at Caterpillar, advancements in physical AI, simulation, sensing, and edge computing are turning things that once felt impossible—intelligent machines operating in dynamic jobsites—into reality.
Our connected ecosystem brings together massive volumes of high-quality data to create a foundation where engineers like you can build and deploy against.
If this work motivates you, we invite you to join our team. In these roles, you’ll work at the intersection of the physical and digital worlds. You’ll help design and deliver intelligent systems that enable machines to perceive their environment, make informed decisions, and support safer, more productive operations.
Apply today to build the new era of construction autonomy at Caterpillar.
Job Summary
As a Lead Data Engineer, you will design, build, and maintain scalable data pipelines, microservices, and cloud-based data platforms that deliver reliable, high-quality data for business and engineering teams. Working in an agile environment, you will help drive data architecture, performance, reliability, and continuous improvement across modern data solutions.
What You Will Do:
- Actively collaborate with Principal Software Engineers and Data Architects to define solution architecture
- Lead the solution design and optimization of scalable data pipelines and microservices in Python, enabling both real-time and batch data processing across enterprise platforms
- Drive the development of cloud-native data ingestion and streaming solutions leveraging AWS services including Kinesis, S3, DynamoDB, EventBridge, and related technologies
- Own the design, implementation, and operational excellence of data integration frameworks and source data pipelines supporting CI Autonomy initiatives
- Partner with business, product, and engineering stakeholders to translate complex requirements into scalable data architectures, workflows, mappings, and system designs
- Establish and enforce automated testing, data quality controls, and validation frameworks to ensure integrity, reliability, and compliance across distributed data ecosystems
- Lead operational monitoring, performance tuning, and root-cause analysis of production data platforms using observability tools such as CloudWatch to maintain high availability and service reliability
What You Will Have:
- Decision Making and Critical Thinking: Ability to lead the analysis and resolution of complex issues within distributed data platforms, designing scalable, and resilient solutions
- Effective Communications: Ability to communicate across teams by sharing feedback constructively, listening to others, and creating documentation that makes data systems and processes easy to understand and support
- Software Development: Experience in leading the design and development of backend systems and data pipelines using Python, Java, and modern frameworks, providing technical directions and ensuring the delivery of reliable, scalable solutions
- Software Development Life Cycle: Experience leading the delivery of data engineering solutions in an Agile environment by guiding work through the full development lifecycle, translating requirements into technical solutions, and ensuring projects are delivered with quality, reliability, and business value
- Software Integration Engineering: Capability to lead the design and integration of APIs, data pipelines, streaming platforms, and databases to enable reliable data exchange across enterprise systems and partner platforms
- Software Product Design/Architecture: Expertise leading the design of scalable, event-driven data systems and architectures, guiding technical decisions and ensuring solutions are reliable, maintainable, and aligned with business needs.
- Software Product Technical Knowledge: Ability to apply strong knowledge of AWS services and data engineering tools to define requirements, support testing and deployment activities, troubleshoot issues, and ensure data solutions are configured, implemented, and operated effectively across environments
- Software Product Testing: Ability to define and implement testing strategies, including functional, performance, and data quality testing, to ensure reliable, scalable, and high-performing data solutions across the development lifecycle.
Top Candidates Will Have:
- Bachelor’s degree in Computer Science, Computer Engineering, or related field
- 8+ years of experience in data engineering or related disciplines with increasing responsibility
- Extensive experience on modern, large scale, complex Caterpillar data platforms such as Helios Data Platform
- Strong foundation developing and deploying Python solutions to a production environment
- Experience leading teams to build high-throughput, scalable data pipelines
- Strong hands-on experience with AWS data services (Kinesis, S3, DynamoDB, EventBridge, etc.) at scale
- Strong in SQL, including data quality and validation practices
- Experience in deploying software using CI/CD tools such as Azure DevOps, Jira, Jenkins, etc.
- Experience developing microservices that support real-time data ingestion
- Experience developing software applications using relational and noSQL databases
- Ability to ensure data integrity across distributed and streaming systems
- Experience with monitoring, testing, and automation in large-scale data environments
Summary Pay Range:
$128,470.00 - $208,770.00Compensation and benefits offered may vary depending on multiple individualized factors, job level, market location, job-related knowledge, skills, individual performance and experience. Please note that salary is only one component of total compensation at Caterpillar.
Benefits:
Subject to plan eligibility, terms, and guidelines. This is a summary list of benefits.
Medical, dental, and vision benefits*
Paid time off plan (Vacation, Holidays, Volunteer, etc.)*
401(k) savings plans*
Health Savings Account (HSA)*
Flexible Spending Accounts (FSAs)*
Health Lifestyle Programs*
Employee Assistance Program*
Voluntary Benefits and Employee Discounts*
Career Development*
Incentive bonus*
Disability benefits
Life Insurance
Parental leave
Adoption benefits
Tuition Reimbursement
- These benefits also apply to part-time employees
Relocation is available for this position. Visa sponsorship is available for eligible applicants.
Posting Dates:
Any offer of employment is conditioned upon the successful completion of a drug screen.
Caterpillar is an Equal Opportunity Employer, Including Veterans and Individuals with Disabilities. Qualified applicants of any age are encouraged to apply.
Not ready to apply? Join our Talent Community.
Data Platform Engineer Jobs by Experience Level
Top Cities Hiring Data Platform Engineers
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Find Data Platform Engineer JobsData Platform Engineer Job Market
Who's Hiring
- Apple14

- Amazon10

- TikTok8

- JPMorganChase7

- GEICO6

Top Industries Hiring
- Technology & Software69
- Banking & Financial Services16
- Consulting & Professional Services9
- Electronics & Hardware7
- Retail6
What Employers Look For
The qualifications that appear most often in data platform engineer jobs.
- 5+ years of experience designing and building scalable data pipelines and ETL workflows
- Proficiency with cloud data platforms such as Snowflake, BigQuery, or Redshift
- Hands-on experience with orchestration tools including Apache Airflow or Prefect
- Strong SQL skills and experience with dbt for data transformation and modeling
- Experience with streaming technologies such as Apache Kafka or Apache Spark
- Bachelor's degree in Computer Science, Data Engineering, or a related technical field
Tips for Your Data Platform Engineer Job Search
Tailor your resume to each stack
Generic data engineer resumes get screened out fast. Match the tools you list to each job description: if the role specifies dbt, Airflow, and Snowflake, those names need to appear in your bullet points with measurable outcomes tied to each.
Show data lineage work on GitHub
Hiring managers for platform roles check public repos. Push a project that demonstrates end-to-end pipeline design with documentation, not just raw scripts. Lineage diagrams and schema versioning examples set your portfolio apart from candidates who only list tools.
Apply early to roles that fit
Migrate Mate lists data platform engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Target companies by their data maturity
A seed-stage startup needs a builder who sets up infrastructure from scratch. A public company with a mature platform needs someone who optimizes and scales. Reading the job description for phrases like 'greenfield' or 'modernize existing pipelines' tells you which mode you're walking into.
Prepare a system design answer on streaming vs batch
Almost every technical screen for this role includes a data architecture question. Walk through your decision process for choosing Kafka over scheduled batch jobs, or vice versa, with a real tradeoff you've navigated. Interviewers want reasoning, not definitions.
Negotiate by anchoring to cloud cost savings
Data platform engineers directly affect infrastructure spend. Before your offer conversation, quantify cost reductions you've driven through query optimization, partitioning, or storage tiering. Framing your value in dollars your previous employer saved gives you concrete leverage.
Data Platform Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most data platform engineers?
The companies hiring the most data platform engineers right now include Apple, Amazon, and TikTok, with the largest share of openings in California, New York, and Texas, based on current listings on Migrate Mate as of August 2026. Demand is especially strong at companies undergoing cloud migrations or building out self-serve analytics capabilities.
How many data platform engineer jobs are remote?
About 64% of data platform engineer openings are fully remote or hybrid as of August 2026, making it one of the more remote-accessible infrastructure roles. Sub-areas like cloud data warehouse engineering and pipeline development tend to have the highest share of fully distributed positions, while roles focused on real-time streaming infrastructure more often require on-site collaboration.
How do you become a data platform engineer?
Start by building strong SQL skills and learning a cloud platform like Snowflake, BigQuery, or Redshift. Add hands-on experience with an orchestration tool such as Apache Airflow by building personal projects or contributing to open-source pipelines. Progress through data analyst or junior data engineer roles to develop production experience, then deepen expertise in data modeling with dbt and distributed processing frameworks like Spark to move into senior platform work.
How do you get a data platform engineering job with little experience?
Build a public portfolio that shows pipeline design, not just analysis. Create an end-to-end project using free cloud tiers of Snowflake or BigQuery, schedule it with Airflow, and document it thoroughly on GitHub. Entry-level openings often favor candidates who can demonstrate they've shipped something functional over those with only coursework. Roles titled junior data engineer or analytics engineer are the most common entry points into platform work.
What does the data platform engineer interview process look like?
Most interview processes start with a recruiter screen, followed by a technical phone interview testing SQL and basic pipeline design. A take-home or live coding exercise focused on data modeling or pipeline architecture usually comes next. The final round typically includes a system design session where you walk through building or scaling a data platform, plus a behavioral round with the engineering manager and cross-functional stakeholders.
Where can I find and apply to data platform engineer jobs?
You can find and apply to data platform engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your experience, tools, and preferred location or remote setup, then apply directly to each one that fits.
See All 427+ Data Platform Engineer Jobs
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