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Remote Data Jobs in Alaska (NOW HIRING)

Sedgwick

Data Engineer AI

Minto, AK · On-site +1

$118K - $142K/yr

You are a "day-one" builder who ensures that data is not just moved, but engineered for the ... LI-TS1 #remote Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace. If you're ...

CVS Health

Call Center Service Advocate I - Remote

Homer, AK · Remote

$16 - $20.75/hr

Data Entry: Accurately enter patient information and appointment details into the scheduling system ... Remote office setting with a focus on phone-based interactions. * Hours: Assigned schedules of 40 ...

micro1 AI

Private Funds Attorney - Remote

Anchorage, AK · Remote

$90 - $130/hr

Remote We are seeking seasoned Funds Attorneys for a part-time role at the forefront of legal AI ... Collaborate with product and research teams to refine data, guidelines, and best practices for AI ...

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All JobsData JobsRemote Data JobsRemote Data Jobs in Alaska

Remote Data information

See Alaska salary details

$49.5K

$177.7K

$262.2K

How much do remote data jobs pay per year?

As of Aug 1, 2026, the average yearly pay for remote data in Alaska is $177,715.00, according to ZipRecruiter salary data. Most workers in this role earn between $143,800.00 and $183,100.00 per year, depending on experience, location, and employer.

What are some common challenges faced when working as a Remote Data Analyst, and how can they be addressed?

Remote Data Analysts often face challenges such as maintaining effective communication with team members, managing access to secure data, and staying aligned with project goals across different time zones. These can be addressed by leveraging collaboration tools like Slack or Microsoft Teams, following strict data security protocols, and participating in regular virtual meetings to ensure everyone is on the same page. Proactive communication and strong organizational skills are key to thriving in a remote data role.

What is the difference between Remote Data vs Remote Data Analyst?

AspectRemote DataRemote Data Analyst
Required CredentialsBachelor's in Data Science, Computer Science, or related field; knowledge of databases and data toolsBachelor's in Data Science, Statistics, or related; proficiency in data analysis tools like Excel, SQL, and visualization software
Work EnvironmentRemote, often independent, with collaboration via online platformsRemote, involves analyzing data sets, creating reports, and communicating findings
Employer & Industry UsageTech companies, finance, healthcare, and e-commerceBusiness, marketing, finance, and tech sectors

Remote Data generally refers to roles focused on managing and processing data, while Remote Data Analyst emphasizes analyzing data to generate insights. Both roles often require similar educational backgrounds and work remotely, but Data Analysts typically focus more on interpreting data and creating reports for decision-making.

How can I make 2000 a week working from home?

Remote data roles such as data analyst or data scientist can offer high earning potential, with experienced professionals earning $2,000 or more weekly through project-based work, consulting, or full-time employment. Building skills in data analysis tools, programming languages, and obtaining relevant certifications can help increase earning capacity, especially when working independently or in specialized niches.

Are there real remote data entry jobs?

Yes, remote data entry jobs are available and involve inputting information into digital systems from home. These roles typically require basic computer skills, attention to detail, and sometimes familiarity with spreadsheet or database software. Legitimate positions are often posted on reputable job boards and do not require upfront fees.

How to make $1000 a week remotely?

Remote data roles such as data analyst or data scientist can generate $1000 or more weekly with experience, strong analytical skills, and proficiency in tools like Excel, SQL, or Python. Achieving this income often involves freelance projects, contract work, or full-time positions with high pay rates, and may require certifications or specialized knowledge in data management and analysis.

What are the key skills and qualifications needed to thrive as a Remote Data Analyst, and why are they important?

To thrive as a Remote Data Analyst, you need strong analytical skills, proficiency in statistics, and a background in data science or a related field. Familiarity with data analysis tools such as Python, R, SQL, and platforms like Tableau or Power BI, along with relevant certifications, is typically required. Excellent self-motivation, time management, and communication skills help you stand out in a remote environment. These capabilities are crucial for delivering accurate insights, collaborating effectively from a distance, and meeting business objectives efficiently.

Is 40 too late for data science?

Age is not a barrier to entering data science, and many professionals start or transition into the field later in life. Success depends on acquiring relevant skills such as programming, statistics, and machine learning, often through online courses or certifications, regardless of age.

What are remote data jobs?

Remote data jobs are positions that involve collecting, analyzing, managing, or interpreting data while working from a location outside the traditional office environment. These roles can include data analysts, data scientists, data entry specialists, and database administrators, among others. Remote data professionals use online tools and platforms to access and process data, collaborate with teams, and deliver insights or reports. This flexible work arrangement allows individuals to contribute to data-driven projects from anywhere with an internet connection.
What are the most commonly searched types of Data jobs in Alaska? The most popular types of Data jobs in Alaska are:
What are popular job titles related to Remote Data jobs in Alaska? For Remote Data jobs in Alaska, the most frequently searched job titles are:
What job categories do people searching Remote Data jobs in Alaska look for? The top searched job categories for Remote Data jobs in Alaska are:
What cities in Alaska are hiring for Remote Data jobs? Cities in Alaska with the most Remote Data job openings:
Remote Data jobs near you
Infographic showing various Remote Data job openings in Alaska as of July 2026, with employment types broken down into 1% As Needed, 82% Full Time, 11% Part Time, 1% Temporary, and 5% Contract. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $177,715 per year, or $85.4 per hour.

Data Engineer AI

Sedgwick

Minto, AK • On-site, Remote

$118K - $142K/yr

Other

This job post has expired 1 day ago. Applications are no longer accepted.

Sedgwick rating

7.6

Company rating: 7.6 out of 10

Based on 319 frontline employees who took The Breakroom Quiz

205th of 300 rated insurance

Job description

By joining Sedgwick, you'll be part of something truly meaningful. It's what our 33,000 colleagues do every day for people around the world who are facing the unexpected. We invite you to grow your career with us, experience our caring culture, and enjoy work-life balance. Here, there's no limit to what you can achieve.

Newsweek Recognizes Sedgwick as America's Greatest Workplaces National Top Companies

Certified as a Great Place to Work

Fortune Best Workplaces in Financial Services & Insurance

Data Engineer AI

Role Overview

As a Senior Data Engineer within the Transformation Office, you are the hands-on architect of the data supply chain for our most advanced initiatives. You will be responsible for the "heavy lifting" required to fuel Data Science models and AI applications with high-fidelity data. Your mission is to build the pipelines that bridge our legacy on-prem systems (Mainframes, SQL Server, DB2) with our modern Snowflake environment and AWS/Azure AI stacks. You are a "day-one" builder who ensures that data is not just moved, but engineered for the specific requirements of model training, feature stores, and RAG-based AI systems.

Key Responsibilities

Hybrid Data Pipeline Execution: Design and implement robust ETL/ELT pipelines to ingest data from legacy on-prem sources, AWS (S3/RDS), and Azure (Blob/SQL), centralizing it for consumption in Snowflake and AI services.

Engineering for Data Science: Build and maintain Feature Stores and specialized datasets optimized for machine learning, ensuring Data Scientists have immediate access to clean, versioned, and statistically valid data.

Engineering for AI (RAG & LLMs): Develop the data pipelines required for Generative AI, including the automated extraction, chunking, and loading of unstructured data into vector stores across AWS and Azure.

Snowflake Power-User Execution: Act as the technical lead for our Snowflake data warehouse, implementing sophisticated data modeling, Snowpipe automation, and compute optimization to support high-concurrency AI workloads.

Legacy "Back-Reach" Engineering: Execute non-invasive data extraction patterns to unlock mission-critical data from decades-old on-premise systems without disrupting core business operations.

Multi-Cloud Orchestration: Manage complex, cross-platform data workflows using Airflow, Step Functions, or Azure Data Factory, ensuring the synchronization of data across our multi-cloud AI posture.

IT & Security Diplomacy: Partner directly with central IT, Database Administrators, and Security teams to solve connectivity hurdles (PrivateLink, IAM, firewalls) and secure "license to operate" for new data flows.

Data Quality for Model Integrity: Implement automated validation and observability layers to detect data drift and quality issues that could compromise the accuracy of production AI and Data Science models.

Cost & Performance Management: Drive the efficiency of our data stack by optimizing storage and query performance in Snowflake, AWS, and Azure to manage the ROI of the Transformation Office.

Direct Stakeholder Collaboration: Work as a dedicated engineering partner to MLOps and Data Science teams to rapidly iterate on data requirements for evolving AI use cases.

Qualifications

Education: Bachelor's degree in Computer Science, Data Engineering, or a related field is required. A Master's degree is highly desirable.

Proven Execution: 6+ years of hands-on data engineering experience, with a track record of building production-grade pipelines for Data Science and AI in multi-cloud environments.

Snowflake Mastery: Expert-level proficiency in Snowflake architecture, including data sharing, performance tuning, and the integration of Snowflake with external cloud AI services.

Multi-Cloud Proficiency: Advanced, hands-on knowledge of AWS (S3, Glue, Lambda) and Azure (Data Factory, Synapse) data services.

Technical Stack: Mastery of Python, SQL, and PySpark. Deep experience with data orchestration and containerization (Docker).

Legacy Expertise: Proven ability to interface with "old world" tech (on-premise SQL, Mainframe extracts, flat files) and transform it for modern cloud consumption.

AI/DS Fluency: A strong understanding of the specific data needs for Machine Learning (feature engineering) and Generative AI (vectorization and embedding pipelines).

Execution Mindset: A "get-it-done" attitude, capable of navigating enterprise bureaucracy and technical debt to ship code at the speed required by a Transformation Office.

#LI-TS1 #remote

Sedgwickis an Equal Opportunity Employer and a Drug-Free Workplace.

If you're excited about this role but your experience doesn't align perfectly with every qualification in the job description, consider applying for it anyway! Sedgwick is building a diverse, equitable, and inclusive workplace and recognizes that each person possesses a unique combination of skills, knowledge, and experience. You may be just the right candidate for this or other roles.

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