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Contract Data Engineering Jobs in Bothell, WA (NOW HIRING)

LVT

Staff Data Engineer

Seattle, WA · On-site

$130K - $156K/yr

Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality gates and mentor IC work toward them, growing the function as the team forms. OUR IDEAL CANDIDATE

LVT

Staff Data Engineer

Seattle, WA

$130K - $156K/yr

Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality gates and mentor IC work toward them, growing the function as the team forms. OUR IDEAL CANDIDATE

Weyerhauser Co

Data Engineer

Seattle, WA · On-site

$130K - $156K/yr

Drive engineering best practices - version control, testing, documentation, observability, and ... contracts, and runbooks so the platform is supportable by the broader team. * Mentor junior ...

Weyerhaeuser Company

Data Engineer

Seattle, WA

$130K - $156K/yr

Drive engineering best practices - version control, testing, documentation, observability, and ... contracts, and runbooks so the platform is supportable by the broader team. * Mentor junior ...

Software Technology Inc

Data Scientist

Seattle, WA · On-site

Contract (C2C) Microsoft Fabric , Data scientist, Agentic AI Experience15 Years Start Date: ASAP ... Work with Microsoft Fabric for data engineering, analytics, and AI-driven insights. * Develop ...

TEKFORTUNE INC

Sr. Data/Cloud Engineer

Seattle, WA · On-site

$130K - $156K/yr

Required : • 5-8 years of hands-on experience in data engineering, cloud data platforms, and ETL ... contracts, and technical specifications. • Experience working in Agile/Scrum delivery models.

Apolis

SQL / Python Data Analyst

Redmond, WA · On-site

Redmond WA (Hybrid) Duration: 6+ months contract (possible extension) W2, & without sponsorship now ... Strong Python programming skills for data analysis, automation, and data engineering tasks.

New

TekWissen LLC

Data Engineer III

Redmond, WA · On-site

$83/hr

Contract Work Type: Onsite Client is looking for a Data Scientist to join the Kuiper Production ... In this role you will help engineering and production stakeholders quickly access the available ...

Nscale

Data Engineer - Seattle, WA

Seattle, WA

$160K/yr

You'll work closely with Operations, Infrastructure, Platform Engineering, Product, and Commercial ... establishing clear data contracts, documentation, and semantic layers. ● Build use cases ...

Haus

Staff Engineer - Data Platform - Seattle

Seattle, WA · On-site

$240K - $260K/yr

... data contracts. * Excellent written and verbal communication; able to defend technical decisions to engineering, product, and exec stakeholders. Bonus Points * Background contributing to or ...

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Showing results 21-40

All JobsContract Data Engineering JobsContract Data Engineering Jobs in Bothell, WA

Contract Data Engineering information

See Bothell, WA salary details

$49.7K

$145K

$198.4K

How much do contract data engineering jobs pay per year?

As of Aug 17, 2026, the average yearly pay for contract data engineering in Bothell, WA is $145,008.00, according to ZipRecruiter salary data. Most workers in this role earn between $128,000.00 and $153,700.00 per year, depending on experience, location, and employer.

What is contract data engineering?

Contract data engineering refers to hiring data engineers on a temporary or project basis, rather than as full-time employees. Contract data engineers are responsible for designing, building, and maintaining data pipelines, databases, and other infrastructure to support data analytics and business needs. Companies often hire contract data engineers to handle specific projects, scale up teams quickly, or bring in specialized skills for a limited time. This arrangement offers flexibility for both the company and the engineer, and is common in industries with fluctuating data workloads or short-term projects.

What are the key skills and qualifications needed to thrive as a contract data engineer?

To thrive as a Contract Data Engineer, you need strong proficiency in data modeling, ETL processes, and programming languages such as Python or SQL, often supported by a degree in computer science or a related field. Familiarity with big data platforms (e.g., Hadoop, Spark), cloud services (AWS, Azure, GCP), and relevant certifications like Google Cloud Professional Data Engineer are typically required. Excellent problem-solving, adaptability, and effective communication are crucial soft skills in this role. These competencies enable efficient project delivery, seamless collaboration with stakeholders, and the ability to quickly adapt to new technical environments and client requirements.

What are some common challenges faced by contract data engineers and how can they be addressed?

Contract data engineers often face the challenge of quickly familiarizing themselves with a company's existing data infrastructure and processes. Since contracts are typically short-term, there is limited time to onboard, understand unique data pipelines, and build relationships with stakeholders. To address this, successful contract data engineers proactively communicate with team members, document their work thoroughly, and leverage their prior experience with a variety of tools and platforms. Flexibility and strong problem-solving skills are essential for adapting to new environments and delivering results efficiently.

What is the difference between Contract Data Engineering vs Data Analyst?

AspectContract Data EngineeringData Analyst
Required SkillsSQL, Python, ETL, cloud platforms, data pipeline developmentSQL, Excel, data visualization, reporting tools
Work EnvironmentProject-based, technical teams, cloud or on-premises infrastructureBusiness units, reporting teams, often in office or remote
Industry UsageTech, finance, healthcare, retailMarketing, finance, healthcare, retail

Contract Data Engineers focus on building and maintaining data pipelines and infrastructure, requiring technical skills in programming and cloud platforms. Data Analysts interpret data, create reports, and visualize insights, often using different tools. While both roles work with data, Contract Data Engineering is more technical and infrastructure-oriented, whereas Data Analysts focus on data interpretation and business insights.

What are the most commonly searched types of Data Engineering jobs in Bothell, WA?

The most popular types of Data Engineering jobs in Bothell, WA are:

What are popular job titles related to Contract Data Engineering jobs in Bothell, WA?

For Contract Data Engineering jobs in Bothell, WA, the most frequently searched job titles are:

What job categories do people searching Contract Data Engineering jobs in Bothell, WA look for?

The top searched job categories for Contract Data Engineering jobs in Bothell, WA are:

What cities near Bothell, WA are hiring for Contract Data Engineering jobs?

Cities near Bothell, WA with the most Contract Data Engineering job openings:

Contract Data Engineering jobs near you
Infographic showing various Contract Data Engineering job openings in Bothell, WA as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 17% Part Time, and 4% Contract. Highlights an 85% Physical, 5% Hybrid, and 10% Remote job distribution, with an average salary of $145,008 per year, or $69.7 per hour.

Staff Data Engineer

LVT

Seattle, WA • On-site

$130K - $156K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Re-posted 26 days ago

Job description

ABOUT LVT
LVT is redefining how businesses operate in the physical world, moving beyond traditional security solutions to deliver AI-driven, actionable intelligence that makes sites smarter, safer, and more secure. Since pioneering our first mobile, solar-powered units, our commitment to scrappy, hands-on innovation has made us an established leader and one of the fastest-growing companies in intelligent site technology. We are building the next generation of solutions-from our physical units in the field to a powerful Agentic AI platform-that allows our customers to gain unprecedented visibility and control over safety, compliance, and operations. This is your chance to join a cutting-edge team that isn't just watching the world change, but actively building the technology that is changing it.
We're a team that's focused on growth and innovation, and we're proud that our crew, products, and leadership are being recognized for it.
  • A Top-Tier Growth Company: Named one of the Financial Times' Fastest Growing Companies 2025 and #10 on the Inc. 5000 Rocky Mountain Regional list for 2025.
  • Innovative Leadership: Our CEO, Ryan Porter, was named an EY Entrepreneur of the Year 2025, and our CTO, Steve Lindsey, was inducted into the Silicon Slopes CTO Hall of Fame in 2024.
  • Product & Software Excellence: We were named one of The Software Report's Top 100 Software Companies of 2023 and are a winner of the Security Today Govies Award for 2025.

ABOUT THIS ROLE
LVT's AI systems are only as good as the data behind them. As we move toward Physical AI, the binding constraint shifts from model architecture to the data flywheel.
We are seeking a Staff Data Engineer to own that flywheel end to end including logs, sensor telemetry, labels and annotations, evaluation and benchmark sets. Every AI team trains and evaluates from a single stack that transforms data from the raw source through standardized, versioned, governed datasets.
This is a senior individual-contributor and technical-leadership role; formal people management is not required. You will partner closely with AI/ML research, the ML platform / MLOps function. You own the data side of the contract that defines what a model consumes and emits and annotation, edge, and infrastructure teams. You should be equally comfortable discussing dataset schema design, storage and partitioning trade-offs for multimodal data, versioning and migration strategy, and the governance controls that keep sensitive video and sensor data safe.
ROLE RESPONSIBILITIES
  • Data Flywheel Ownership: Own the end-to-end loop that converts raw edge telemetry and video into labeled training data, frozen evaluation sets and feeds model outputs back into the next round.
  • Layered Dataset Pipelines: Build and own the pipelines that register raw source data, standardize it into a single well-defined schema, and join and aggregate it into curated datasets so every team trains, validates, and benchmarks from one consistent store through one reader, rather than copying and reformatting data per use case.
  • Labels & Annotation Data Lifecycle: Own how labels and semantic annotations are appended to datasets without rewriting source data, then versioned, quality-checked, and served, partnering with annotation and data-operations teams on label production and verification while you own the dataset, storage, and serving side.
  • Evaluation & Benchmark Sets: Own the frozen, versioned validation and benchmark datasets that make model comparisons valid over time stable enough that an accuracy delta reflects the model, not a shifting dataset including the review and scrubbing discipline required before any set is shared externally.
  • Dataset Versioning: Own schema and content versioning so producers can evolve datasets without breaking consumers opt-in versions, append-without-rewrite for new fields, and the reader/writer indirection that lets data migrate underneath clients on a controlled rollout instead of forced lockstep migrations.
  • Framework Integration & Self-Serve Access: Own the read/write libraries and integrations researchers depend on PyTorch/Lightning dataloaders, a simple record-level CRUDL API, and Spark/analytics access and self-service so AI teams stay focused on model development.
  • Governance Enforced: Make governance machine-enforced in the flywheel rather than documented after the fact classification of clips, frames, labels, and embeddings; scrubbing and anonymization in load jobs; and lineage and provenance for every dataset version, annotation campaign, and training input.
  • Technical Mentorship: Set the data-engineering standards for the flywheel schema conventions, dataset contracts, quality gates and mentor IC work toward them, growing the function as the team forms.

OUR IDEAL CANDIDATE
  • Data Engineering Depth: 8+ years building and operating large-scale data pipelines and data-lake or lakehouse systems in production ingestion, ETL/ELT, partitioning and storage-format decisions, and the reader/writer libraries consumers rely on.
  • ML Data Specialty: Has built data pipelines for model training and evaluation, labeled data, and evaluation/benchmark sets with a working understanding of how data quality and versioning move model results.
  • Lakehouse Architecture: Strong experience with medallion-style layered data architectures and modern table/lake formats (e.g. Iceberg, Delta, Parquet, or comparable), including schema evolution and dataset versioning.
  • Multimodal Data at Scale: Experience with large multimodal data video, image, sensor/telemetry and the storage and access patterns that make it queryable at scale (denesting, repartitioning, binary-inline vs. reference storage).
  • Framework Integration: Hands-on with the data side of ML frameworks PyTorch/Lightning dataloaders and Spark and strong Python knowledge.
  • Governance & Provenance: Practical experience enforcing data governance in pipelines classification, access control, lineage and provenance, retention, particularly for privacy sensitive data.
  • Technical Leadership: A track record of setting data-engineering direction and leveling up engineers (technical leadership; formal management not required).
  • Education: Bachelor's or Master's in Computer Science, Engineering, or a related field, or equivalent practical experience.

PREFERRED QUALIFICATIONS
  • Streaming or near-real-time ingestion from edge/IoT sources into a data lake (e.g. Kafka, Lambda, EMR, or similar).
  • Append-without-rewrite and hash-indexed dataset techniques on open table formats, and dataset/feature-versioning systems.
  • Generative-AI data work: fine-tuning and evaluation dataset curation for LLMs/VLMs.
  • Exposing datasets to AI agents through MCP-style query interfaces, with semantic schema and plain-language documentation for retrieval.
  • Computer-vision / video annotation tooling and workflows (e.g. Encord, Labelbox, or similar).

COMPENSATION
The beginning annual salary range for this role is $171,900 - $221,000 USD and is determined by location, job-related experience, and education/training. Your total earning potential is amplified by a bonus structure tied to meeting goals, and you will become an owner from day one through our employee equity program.
BENEFITS
We believe you do your best work when your whole life is supported. We invest in our crew's health, families, and financial futures with a benefits package designed to support you inside and outside the office. Full-time benefits include, but not limited to: Comprehensive health, dental and vision coverage, retirement benefits (401k match up to 4%), and flexible PTO.
LVT IS PROUD TO BE AN EQUAL OPPORTUNITY EMPLOYER. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status. All candidates must pass a drug screening and background check upon employment. Some roles may also require passing a federal background check and fingerprinting. Must be authorized to work in the U.S. If reasonable accommodation is needed to participate in the job application or interview process, and/or to perform essential job functions, please reach out to your recruiter.

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