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Tecton Jobs (NOW HIRING)

84.51˚

Senior ML Data Engineer (P508)

Cincinnati, OH · On-site

$103.40K - $140.50K/yr

... Tecton, or similar enterprise solutions • Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices • Multi-cloud experience with both ...

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 ...

Farmers Inc

ML Ops Enterprise Architect

Spokane, WA

Tecton, Databricks, FeatureForm). 3+ years : DevOps (Eg. Argo CD / Argo Workflows), Containerization (Kubernetes, ROSA). 3+ years : Enterprise Application Integration (Eg. Guidewire, Salesforce). 4+ ...

Group1001

Staff ML Engineer

Zionsville, IN

Experience with feature stores (SageMaker Feature Store, Feast, Tecton, or similar); designing feature pipelines for both batch and real-time serving * Experiment Tracking & Registry: MLflow, Weights ...

84.51

Senior ML Data Engineer

Manhattan, NY

$116.90K - $158.70K/yr

Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutions Deep knowledge of point-in-time correctness principles, temporal joins ...

Tebra

Senior Data Engineer

Manhattan, NY · On-site

$116.90K - $158.70K/yr

Experience implementing or managing a Feature Store (e.g., Feast, Tecton). Familiarity with Data Versioning Control tools (e.g., DVC, LakeFS). Published research or conference papers in data ...

New

Tebra

Senior Data Engineer

$108.50K - $147.40K/yr

Experience implementing or managing a Feature Store (e.g., Feast, Tecton). * Familiarity with Data Versioning Control tools (e.g., DVC, LakeFS). * Published research or conference papers in data ...

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How much do tecton jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for tecton in the United States is $26.34, according to ZipRecruiter salary data. Most workers in this role earn between $15.14 and $30.77 per hour, depending on experience, location, and employer.

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 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 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.

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What states have the most Tecton jobs? States with the most job openings for Tecton jobs include:
What job categories do people searching Tecton jobs look for? The top searched job categories for Tecton jobs are:
Tecton jobs near you
Infographic showing various Tecton job openings in the United States as of May 2026, with employment types broken down into 94% Full Time, and 6% Contract. Highlights an 83% Physical, 7% Hybrid, and 10% Remote job distribution, with an average salary of $54,791 per year, or $26.3 per hour.
Senior ML Data Engineer (P508)

Senior ML Data Engineer (P508)

84.51˚

Cincinnati, OH • On-site

$103.40K - $140.50K/yr

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Summary:
84.51° is a retail data science, insights and media company. They are seeking a Senior ML Data Engineer to architect, build, and operate the data infrastructure that powers their machine learning models, focusing on feature engineering and production data systems.
Responsibilities:
• Own the feature request lifecycle from intake through deployment, driving reusability and maintaining a searchable feature catalog
• Design and build scalable feature pipelines that compute features from diverse sources (BigQuery, Azure Data Lake) and write to Feature Store infrastructure (Vertex AI Feature Store + BigQuery)
• Build streaming feature engineering pipelines using Apache Beam/Dataflow for real time feature computation and low-latency model serving with sub-second data freshness
• Ensure point-in-time correctness and online/offline feature consistency to prevent data leakage
• Implement drift detection, data quality monitoring, and alerting mechanisms
• Develop self-service tools and templates that enable teams to independently create features
• Build automated pipelines that generate ML-ready training datasets by combining features with labeled target variables
• Implement point-in-time correctness logic and sophisticated sampling strategies to ensure balanced, representative datasets
• Maintain comprehensive dataset versioning for full traceability across model versions
• Generate detailed evaluation reports with performance metrics segmented by business dimensions
• Support operations across both Azure and Vertex AI environments during platform migration
• Serve as Tier 2/3 on-call responder for feature data quality incidents, diagnosing and resolving pipeline failures and performance issues
• Maintain comprehensive lineage tracking and metadata management for full data traceability
• Support regulatory compliance through proper data governance and documentation
• Establish and enforce feature naming conventions, data quality thresholds, and point-in-time correctness patterns
• Conduct workshops on feature engineering best practices and provide expert guidance on feature design
• Partner with Data Scientists, ML Engineers, Data Engineering, and MLOps teams to optimize infrastructure and align with technical strategy
Qualifications:
Required:
• 3+ years of hands-on experience building and maintaining ML data pipelines in production environments with demonstrated expertise in scaling and reliability
• Expert-level SQL skills and advanced Python programming capabilities with experience in data processing frameworks and ML libraries
• Proven experience with cloud data platforms, with strong preference for GCP ecosystem including BigQuery, Dataflow, Vertex AI Feature Store, and associated ML services
• Deep understanding of end-to-end ML workflows including training data preparation, model evaluation methodologies, and serving infrastructure requirements
• Production operations mindset with experience in monitoring, alerting, on-call responsibilities, and meeting SLA commitments
Preferred:
• Hands-on experience with Feature Store platforms such as Vertex AI Feature Store, Feast, Tecton, or similar enterprise solutions
• Deep knowledge of point-in-time correctness principles, temporal joins, and time-series data modeling best practices
• Multi-cloud experience with both Azure and GCP platforms, including data migration and hybrid cloud architectures
• Strong familiarity with core ML concepts including feature engineering, label creation, train/test/validation splits, and data leakage prevention
• Background spanning both analytics engineering and ML-specific data engineering with understanding of the unique requirements of each domain
Company:
84.51° helps companies create sustainable growth by putting the customer at the center of everything. Founded in 2015, the company is headquartered in Cincinnati, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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