Remote Big Data Jobs
Found 77 verified opportunities
Remote Big Data roles sit at the intersection of data engineering and analytics, letting professionals design, ingest, and analyze massive data sets from anywhere with a solid internet connection. Companies across finance, e‑commerce, health‑tech, and telecom rely on engineers who can write efficient SQL queries, orchestrate pipelines with tools like Apache Spark or Hadoop, and translate raw metrics into actionable insights using visualization platforms such as Power BI or Tableau.
Because the work is largely cloud‑based—often on AWS, Azure, or GCP—remote collaboration is natural, and salaries typically range from mid‑level figures in the $70k‑$100k bracket up to six‑figure packages for senior architects, adjusted for experience and regional cost‑of‑living. Employers also value a demonstrable portfolio; a collection of real dashboards, case studies, or open‑source projects shows you can handle end‑to‑end workflows and convinces hiring managers that you’ll deliver value without needing onsite supervision.
Staying current with evolving ecosystems—such as container orchestration with Kubernetes or data lake architectures on Snowflake—helps remote practitioners maintain performance and security, while tools like Slack, Git, and Jira keep distributed teams aligned.
Senior Product Security Analyst - Game Security Team
Sony Interactive Entertainment
Analytics Engineer Sênior
Blip
Associate Data Platform Engineering Manager
Booksy-1
Senior Data Engineer
Hive.co
Data Specialist / Health Analytics Professional
qode
IT Consultant (AI & Machine Learning Specialist)
European Dynamics
Data Analyst / Data Scientist (Autonomous Role) at GitBook
Gitbook
Senior Data Engineer
Firstup
Data Pipeline Engineer
SSC HR
Certification Program Manager (ClickHouse Certified Developer)
Clickhouse
Director, Analytics Systems
Beacon Biosignals
Data Engineer
Netomi
Senior Data Engineer
Leadfeeder
Senior Analyst, Credentialing Quality & Data Operations
Oscar Health
Global Tax Manager
Mariadb
Staff Data Platform Engineer at Vercel
Vercel
Backend Engineer at SplitMetrics Acquire
SplitMetrics
Portfolio Director
Plainconcepts
Portfolio Director
Plain Concepts
Senior Software Engineer
Audiohook
Principal Product Manager
DomainTools
Senior Manager of Data Strategy
Velir
Senior ML Engineer, Identity & Audience
Vibe
Data Scientist
Quora
Frequently Asked Questions
What salary range can I expect for remote Big Data positions?+
Compensation varies by seniority, region, and industry, but most remote Big Data engineers see a base range from roughly $70,000 to $120,000 annually, with senior or architect‑level roles often topping six figures. Bonuses, equity, or location‑adjusted pay can push the total package higher, especially for candidates with proven cloud and pipeline expertise.
Which skills and qualifications are most important for a remote Big Data professional?+
Employers prioritize strong SQL proficiency, hands‑on experience with distributed frameworks like Hadoop or Spark, and the ability to visualize large‑scale results in Power BI or Tableau. A solid grounding in cloud platforms (AWS, Azure, GCP), programming in Python or Scala, and knowledge of data‑modeling concepts round out the core skill set.
How can I break into remote Big Data roles without prior on‑site experience?+
Build a portfolio that showcases end‑to‑end projects: ingest raw logs, process them with Spark, store results in a cloud warehouse, and present insights via an interactive dashboard. Complement that with certifications (e.g., AWS Data Analytics, Google Cloud Professional Data Engineer) and contribute to open‑source or Kaggle datasets to demonstrate real‑world impact to recruiters.
Why is knowledge of distributed processing frameworks such as Hadoop or Spark critical for Big Data jobs, and how does it differ from traditional SQL‑based analysis?+
Distributed frameworks let you scale computations across many nodes, handling terabytes or petabytes that a single‑machine SQL engine cannot process efficiently. They support parallel processing, fault tolerance, and in‑memory analytics, which are essential for real‑time streaming or batch jobs that go beyond the limits of conventional relational databases.