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Tecton Jobs in California (NOW HIRING)

LanceDB Inc

Senior Solutions Engineer

San Francisco, CA · On-site

$180K - $250K/yr

Have worked with feature stores (e.g., Feast, Tecton) or have designed custom ML feature pipelines. * Have experience in observability and monitoring (Prometheus, Grafana, ELK/EFK). * Are familiar ...

Intuit

Senior Staff AI/MLE Scientist

San Diego, CA

$210K - $284K/yr

Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton ...

Intuit

Senior Staff AI/MLE Scientist

San Diego, CA

$210K - $284K/yr

Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton ...

Intuit

Senior Staff AI/MLE Scientist

Mountain View, CA · On-site

$210K - $284K/yr

Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton ...

Intuit

Senior Staff AI/MLE Scientist

Mountain View, CA

$210K - $284K/yr

Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton ...

Intuit

Senior Staff AI/MLE Scientist

Mountain View, CA

$210K - $284K/yr

Strong software engineering fundamentals: experience contributing to and maintaining shared ML libraries, feature stores, or feature engineering frameworks (e.g., featlib, feat-layer, Feast, Tecton ...

All JobsTecton JobsTecton Jobs in California

Tecton information

See California salary details

$8

$25

$60

How much do tecton jobs pay per hour?

As of Jul 20, 2026, the average hourly pay for tecton in California is $25.79, according to ZipRecruiter salary data. Most workers in this role earn between $14.83 and $30.13 per hour, depending on experience, location, and employer.

What is the difference between Tecton vs Data Engineer?

AspectTectonData Engineer
Primary RoleBuilds and manages feature stores for machine learning modelsDesigns, develops, and maintains data pipelines and infrastructure
Skills & CertificationsMachine learning, data engineering, cloud platforms, SQLData pipeline tools, SQL, Python, cloud services
Work EnvironmentCollaborates with data scientists and ML teamsWorks with data engineers, analysts, and software teams
Industry UsageUsed in organizations deploying ML modelsUsed across data-driven companies for data infrastructure

While both Tecton and Data Engineers work with data infrastructure, Tecton specializes in building feature stores for machine learning applications, whereas Data Engineers focus on creating data pipelines and managing data infrastructure for various business needs. The roles often overlap but serve different core functions within data teams.

What are the most common challenges faced by Machine Learning Engineers working with Tecton in deploying real-time features?

Machine Learning Engineers using Tecton often encounter challenges related to integrating real-time feature pipelines with existing data infrastructure and ensuring low-latency performance. Managing data quality, monitoring feature freshness, and coordinating deployments across teams can be complex, especially as models scale to production. Close collaboration with data engineers and DevOps teams is essential for maintaining robust, automated data pipelines and troubleshooting issues quickly.

What are Tecton engineers?

Tecton engineers are professionals who specialize in building and managing feature platforms for machine learning applications. They work with data pipelines, infrastructure, and tools to ensure high-quality, real-time, and batch feature data is accessible for ML models. Tecton engineers often collaborate with data scientists and ML engineers to streamline the process of developing, deploying, and monitoring machine learning features, enabling faster and more reliable AI solutions.

What are the key skills and qualifications needed to thrive as a Machine Learning Platform Engineer at Tecton, and why are they important?

To excel as a Machine Learning Platform Engineer at Tecton, you need a solid background in computer science, data engineering, and machine learning, often demonstrated by a relevant degree and prior experience building data infrastructure. Familiarity with tools like Python, SQL, cloud platforms (AWS, GCP, Azure), and technologies such as Apache Spark or Kubernetes is typically required. Strong problem-solving abilities, collaboration, and effective communication help you work with cross-functional teams and address complex engineering challenges. These skills are vital for designing scalable systems that enable efficient development and deployment of machine learning models.
What are popular job titles related to Tecton jobs in California? For Tecton jobs in California, the most frequently searched job titles are:
What job categories do people searching Tecton jobs in California look for? The top searched job categories for Tecton jobs in California are:
Tecton jobs near you
Infographic showing various Tecton job openings in California as of July 2026, with employment types broken down into 95% Full Time, and 5% Contract. Highlights an 78% Physical, 5% Hybrid, and 17% Remote job distribution, with an average salary of $53,653 per year, or $25.8 per hour.
ML Data Infrastructure Engineer

ML Data Infrastructure Engineer

Redolent, Inc.

Sunnyvale, CA • On-site, Remote

Contractor

Re-posted 17 days ago

Job description

Key Responsibilities:
  • Design and implement scalable data processing pipelines for ML training and validation
  • Build and maintain feature stores with support for both batch and real-time features
  • Develop data quality monitoring, validation, and testing frameworks
  • Create systems for dataset versioning, lineage tracking, and reproducibility
  • Implement automated data documentation and discovery tools
  • Design efficient data storage and access patterns for ML workloads
  • Partner with data scientists to optimize data preparation workflows

Technical Requirements:
  • 7+ years of software engineering experience, with 3+ years in data infrastructure
  • Strong expertise in GCP's data and ML infrastructure:
    • BigQuery for data warehousing
    • Dataflow for data processing
    • Cloud Storage for data lakes
    • Vertex AI Feature Store
    • Cloud Composer (managed Airflow)
    • Dataproc for Spark workloads
  • Deep expertise in data processing frameworks (Spark, Beam, Flink)
  • Experience with feature stores (Feast, Tecton) and data versioning tools
  • Proficiency in Python and SQL
  • Experience with data quality and testing frameworks
  • Knowledge of data pipeline orchestration (Airflow, Dagster)

Nice to Have:
  • Experience with streaming systems (Kafka, Kinesis)
  • Experience with GCP-specific security and IAM best practices
  • Knowledge of Cloud Logging and Cloud Monitoring for data pipelines
  • Familiarity with Cloud Build and Cloud Deploy for CI/CD
  • Experience with streaming systems (Pub/Sub, Dataflow)
  • Knowledge of ML metadata management systems
  • Familiarity with data governance and security requirements
  • Experience with dbt or similar data transformation tools
Redolent logo

About Redolent

Sourced by ZipRecruiter

Redolent, a dynamic and rapidly expanding company committed to excellence in software solutions, where success is fueled by a combination of technical expertise and efficient management practices. Our solutions create a measurable delta in our clients’ productivity and profitability, contributing to their growth and success.

Industry

It services

Company size

51 - 200 Employees

Headquarters location

San Jose, CA, US

Year founded

2008

Social media

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