Data Platform Engineer
8 Oct 2025
Palo Alto, CA, USA
Verified by Turrior
Content + Source + Freshness • 12 Dec 2025 • 95% confidence
80 / 100
Offer value
This role presents solid opportunities for engineers looking to specialize in data infrastructure in a high-growth sector.
- Strong opportunities in data engineering and AI technologies.
- Engagement in collaborative platforms across various domains.
- Involvement in rapidly evolving data strategies.
- Requires substantial technical knowledge and teamwork.
Pros
- Engagement in cutting-edge data management solutions.
- Opportunity to work with diverse data sources and services.
- Collaborative environment across multiple technical disciplines.
Cons
- High expectations for project delivery rates.
- Potentially steep learning curve with new technologies.
- Fast-paced industry with frequent changes.
Who it's for
Mid-level / Senior • In-office or flexible, depending on location.
Good fit
- Mid-level data engineers looking to expand their skill set.
- Candidates experienced in real-time processing technologies.
- Collaborative team players keen on problem-solving.
Not recommended for
- Inexperienced applicants not familiar with data engineering.
- Those uncomfortable in fast-paced environments.
- Candidates preferring solitary roles without much interaction.
Motivation fit
Eagerness to work with advanced data streaming technologies.Desire to contribute to innovative data platforms.Passion for building scalable solutions.
Key skills
Data pipeline developmentSQL and cloud technology proficiencyInfrastructure as Code (IaC) expertiseCollaboration with multidisciplinary teams
Score: 80/100 AI verified analysis
About the job
We are a high-growth company transforming how businesses operate by integrating AI, IoT, and cloud-native services into scalable, real-time platforms. As a Platform Data Engineer, you’ll play a critical role in building and maintaining the data infrastructure that powers our products, services, and insights.
You’ll join a multidisciplinary team focused on ingesting, processing, and managing massive streams of sensor and operational data across a wide array of devices—from drones and robots to industrial systems and smart environments.
Responsibilities
- Design, build, and maintain scalable, reliable, and high-throughput data ingestion pipelines for structured and semi-structured data.
- Implement robust and secure data lake and SQL-based storage architectures optimized for performance and cost.
- Develop and maintain internal tools and frameworks for data ingestion using Python, Golang, and SQL.
- Collaborate cross-functionally with Cloud, Edge, Product, and AI teams to define data contracts, schemas, and retention policies.
- Use AWS cloud infrastructure (including Argo Workflows, S3, Lambda, Glue, Kinesis, Athena, and RDS) to support end-to-end data workflows.
- Employ Infrastructure-as-Code (IaC) practices using Terraform to manage data platform infrastructure.
- Monitor data pipelines for quality, latency, and failures using tools such as CloudWatch, SumoLogic, or DataDog.
- Continuously optimize storage, partitioning, and query performance across large-scale datasets.
- Participate in architecture reviews and ensure the platform adheres to security, compliance, and best practice standards.
Skills and Qualifications
- 5+ years of professional experience in software engineering or data engineering.
- Strong programming skills in Python and Golang.
- Deep understanding of SQL and modern data lake architectures (e.g., using Parquet, Iceberg, or Delta Lake).
- Hands-on experience with AWS services including but not limited to: S3, Lambda, Glue, Kinesis, Athena, and RDS.
- Proficiency with Terraform for automating infrastructure deployment and management.
- Experience working with real-time or batch data ingestion at scale, and designing fault-tolerant ETL/ELT pipelines.
- Familiarity with event-driven architectures and messaging systems like Kafka or Kinesis.
- Strong debugging and optimization skills across cloud, network, and application layers.
- Excellent collaboration, communication, and documentation skills.
Bonus Points
- Experience working with time-series or IoT sensor data at industrial scale.
- Familiarity with analytics tools and data warehouse integration (e.g., Redshift, Snowflake).
- Exposure to gRPC and protobuf-based data contracts.
- Experience supporting ML pipelines and feature stores.
- Working knowledge of Kubernetes concepts.
- Prior startup experience and/or comfort working in fast-paced, iterative environments.
