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Freelance Data Platform Engineer Jobs in California

Data Platform Engineer, Senior Staff

San Diego, CA · On-site

$112K - $152K/yr

We are hiring a Data Platform Engineer to design, build, and operate a modern, data platform with Databricks Lakehouse as a core foundation. This role is ideal for a senior engineer who excels in ...

About the Job We are seeking a Senior/Staff Data Platform Engineer to lead our data infrastructure architecture. The individual filling this position will collaborate with product owners and ...

Role Summary RV Tech is seeking a strong Data Platform Engineer to architect and scale the infrastructure powering the next generation of software‑defined electric vehicles. In this role, you will ...

New

About the Job We are seeking a Senior/Staff Data Platform Engineer to lead our data infrastructure architecture. The individual filling this position will collaborate with product owners and ...

Showing results 41-60

Freelance Data Platform Engineer information

What is a freelance data platform engineer?

A Freelance Data Platform Engineer is a professional who designs, builds, and maintains data infrastructure and platforms on a project or contract basis, rather than as a full-time employee. They work with clients to develop scalable and efficient data solutions, such as data warehouses, ETL pipelines, and cloud-based data systems. Their responsibilities often include integrating various data sources, ensuring data quality, and optimizing data workflows to support analytics and business intelligence. Freelance Data Platform Engineers typically have expertise in databases, cloud services, programming, and data architecture. They offer flexibility and specialized skills to organizations that need temporary or project-based support.

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

To thrive as a Freelance Data Platform Engineer, you need strong skills in data architecture, ETL processes, and cloud platform management, typically supported by a degree in computer science or a related field. Familiarity with tools like SQL, Python, Spark, AWS, Azure, and certifications such as AWS Certified Data Analytics or Google Cloud Data Engineer are highly valuable. Effective client communication, problem-solving, and project management skills help you stand out, especially when coordinating independently with multiple stakeholders. These competencies ensure you can design scalable solutions, deliver projects efficiently, and build strong client relationships in a dynamic freelance environment.

How do freelance data platform engineers typically manage collaboration with clients and remote teams?

Freelance Data Platform Engineers often collaborate with clients and distributed teams using project management tools, version control systems, and regular virtual meetings. Clear communication is essential for aligning on data requirements, setting expectations, and providing progress updates. It's common to work asynchronously, so documenting work and maintaining transparent workflows help ensure smooth handoffs. Building trust and reliability is key to fostering long-term client relationships in a freelance setting.

What is the difference between Freelance Data Platform Engineer vs Data Engineer?

AspectFreelance Data Platform EngineerData Engineer
CredentialsRelevant certifications (e.g., AWS, GCP, Azure), technical skillsSimilar certifications, technical skills, often full-time roles
Work EnvironmentIndependent, project-based, remote or on-siteFull-time, in-house or remote
Employer & Industry UsageFreelance platforms, consulting firms, startupsTech companies, finance, healthcare, large enterprises
Search & Comparison IntentYes, for freelance opportunities or project-based workYes, for full-time or contract roles

In summary, Freelance Data Platform Engineers typically work independently on short-term projects, requiring similar skills and certifications as Data Engineers but with a focus on flexibility and client-based work. Data Engineers often work full-time within organizations, focusing on building and maintaining data infrastructure.

How much do freelance data platform engineers make?

Freelance data platform engineers typically earn between $50 and $150 per hour, depending on experience, skills, and project complexity. Experienced professionals with expertise in cloud platforms, data pipelines, and tools like Apache Spark or Kafka tend to command higher rates. Annual earnings vary widely based on workload and client base, often ranging from $80,000 to over $200,000 for full-time equivalent work.

What are the most commonly searched types of Data Platform Engineer jobs in California?

The most popular types of Data Platform Engineer jobs in California are:

What are popular job titles related to Freelance Data Platform Engineer jobs in California?

For Freelance Data Platform Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Freelance Data Platform Engineer jobs in California look for?

The top searched job categories for Freelance Data Platform Engineer jobs in California are:

What cities in California are hiring for Freelance Data Platform Engineer jobs?

Cities in California with the most Freelance Data Platform Engineer job openings:

Senior Data Platform Engineer

Ellipsis Health, Inc.

San Francisco, CA • On-site

$150 - $170/hr

Other

Medical, Dental, Vision, Retirement, PTO

Re-posted 12 days ago


Job description

Employment Type

Full time

Location Type

Hybrid

Compensation
  • $150K – $170K

We are currently looking for an experienced Senior Data Platform Engineer, with significant experience in building, scaling, and optimizing modern data platforms across public cloud environments.

We are located in the San Francisco Bay Area, but we are open to remote candidates for this role anywhere in the US.

Responsibilities
  • Lead the design, development, and operation of a scalable and secure data platform to support analytics, ML Ops, and business intelligence
  • Collaborate closely with Data Science, Machine Learning, Application and DevOps teams to implement end-to-end ML Ops pipelines
  • Architect and manage data warehousing solutions using Databricks, Dbt, and Spark
  • Develop and maintain ETL/data pipelines that handle structured and unstructured data across diverse sources
  • Optimize data storage, access, and processing for cost-efficiency and performance in GCP and AWS Cloud environments
  • Build and maintain dashboards and analytics solutions using tools such as Sigma, Metabase, and other BI platforms
  • Ensure compliance with data governance, security, and privacy best practices, including HIPAA, SOC-2, and other regulatory requirements
  • Evaluate and integrate third-party anonymization and security solutions to protect sensitive data
  • Provide strategic guidance on the evolution of the data platform to meet the company's growth and technical needs
  • Design and implement scalable infrastructure for Large Language Model (LLM) operations, including training, fine-tuning, and inference workflows
  • Collaborate with AI/ML teams to build and optimize LLM serving platforms for real-time and batch processing
  • Develop monitoring and observability solutions for LLMs, ensuring model performance, cost-efficiency, and compliance with ethical AI guidelines
  • Evaluate and integrate state-of-the‑art LLM technologies into existing data platforms to enhance analytics and decision‑making
Qualifications
  • Bachelor's or Master's Degree in Computer Science or equivalent experience
  • 5+ years of industry experience in designing and building large‑scale data platforms
  • Strong expertise in SQL, Data Modeling, and Data Warehousing (Databricks, Snowflake, Redshift, BigQuery, etc.)
  • Proficiency in writing Advanced SQLs and performance tuning
  • Strong proficiency in Python for building, optimizing, automating and maintaining data pipelines and services
  • Deep experience with Apache Spark and distributed data processing frameworks
  • Hands‑on experience with modern ETL/Orchestration frameworks such as Airflow, dbt, and others
  • Knowledge of business intelligence tools such as Sigma, Metabase, Tableau, and Looker
  • Strong familiarity with cloud‑based infrastructure and managed data services in GCP and AWS Cloud
  • Experience with CI/CD pipelines to automate testing, deployment and release of data engineering and analytics workflows using GitLab, GitHub etc
  • Experience with tools like Kubernetes, Terraform, Pubsub, Debezium
  • Exposure building data quality frameworks and automation
  • Understanding of data governance, privacy, and regulatory frameworks (HIPAA, SOC-2, HITRUST)
Nice to Have
  • Experience working with ML Ops platforms and supporting Data Science teams
  • Experience with ML Ops tools such as MLflow, Streamlit, and vector databases
  • Familiarity with healthcare data standards (FHIR, HL7)
  • Experience in real‑time data processing and event‑driven architectures
  • Expertise in implementing data access controls and anonymization techniques
Salary and Benefits

We offer competitive salary and benefits, including 401(k) matching, health, vision, and dental insurance, and very flexible paid time off.

The typical salary range for this role is $150,000 to $170,000 USD, depending on skills, qualifications, and relevant experience.

As a health technology company, we reserve the right to run background checks on candidates to whom we extend offers, in compliance with applicable laws. We evaluate candidates holistically and comply with all “ban the box” regulations.

Assistance

If you have a disability or require accommodations during the application or recruitment process, please contact careers@ellipsishealth.com.

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