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Why this grade This listing scored 66/100, which is a C. It lost the most ground on pay transparency. See the breakdown
- Description depth 20 / 20 How much the posting actually says about the work, measured in characters of real text.
- Freshness 15 / 15 How recently it was posted. Older postings are likelier to be filled or abandoned.
- Pay transparency 12 / 25 A published salary range, worth more than any other single factor because it is what a candidate cannot find out without applying.
- Remote clarity 8 / 15 Whether "remote" means anywhere, or is quietly restricted to one country.
- Role specificity 6 / 10 Whether the listing is tagged well enough to tell what the role actually is.
- Corroboration 5 / 10 Whether more than one source carries this listing.
Every figure above is arithmetic over the posting itself — its salary field, its text, its age, its tags and how many sources carry it. How the grades work →
This listing does not state a salary
$126k – $163k
That is the middle half of what comparable roles paid on this board over the last 90 days — 39 listings that did publish a figure, median $140k. It is not this employer's offer, and we have no idea what they pay. It is only what the rest of the market advertised.
Data Science Mid level Full Time
About Huzzle
At Huzzle, we connect exceptional talents with top opportunities at leading companies across the UK, US, Canada, Europe, and Australia. Our clients include startups, digital agencies, and tech platforms in industries such as SaaS, MarTech, FinTech, and AI.
Unlike an outsourcing agency, we place you directly with a client where you’re hired in-house as a valued member of their team.
Job Type: Full-time
Location: Remote
Job Summary
As a Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines and infrastructure. You will work closely with data analysts, data scientists, software engineers, and business stakeholders to ensure reliable, accessible, and high-quality data across the organization.
This is an excellent opportunity for professionals who enjoy solving complex data challenges and building systems that support business intelligence, analytics, and machine learning initiatives.
Key Responsibilities
- Design, develop, and maintain scalable ETL and ELT pipelines.
- Build and optimize data architectures, databases, and data warehouses.
- Integrate data from multiple sources, APIs, and third-party platforms.
- Ensure data quality, consistency, reliability, and security.
- Monitor and troubleshoot data pipelines and workflows.
- Collaborate with analytics, engineering, and business teams to understand data requirements.
- Implement data governance and best practices for data management.
- Optimize data storage, processing performance, and query efficiency.
- Support reporting, business intelligence, and analytics initiatives.
- Document data models, workflows, and technical processes.
Requirements
- 3+ years of experience in data engineering, data warehousing, or related roles.
- Strong proficiency in SQL and database management.
- Experience with Python, Java, Scala, or similar programming languages.
- Hands-on experience with ETL/ELT tools and data pipeline development.
- Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Experience with modern data warehouses such as Snowflake, Redshift, BigQuery, or Databricks.
- Familiarity with orchestration tools such as Airflow or Prefect.
- Understanding of data modeling, data governance, and data quality principles.
- Experience working with large datasets and distributed systems is preferred.
- Strong analytical, problem-solving, and communication skills.
- Ability to work independently in a remote, collaborative environment.
Benefits
💰 Competitive salary: Based on experience, technical expertise, and location.
🌎 Fully remote role: Work from anywhere with flexible working arrangements.
🚀 Career growth opportunities: Join innovative companies and work on impactful projects.
📈 Long-term opportunities: Build your career with growing global organizations.
🧠 Continuous learning: Exposure to modern data technologies, cloud platforms, and large-scale data systems.
🤝 Collaborative culture: Work alongside talented engineers, analysts, and business leaders.
⚙️ Cutting-edge technology: Gain experience with modern data stacks and cloud-native solutions.
Originally posted on Himalayas
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Where this listing came from
- 01 Oct 2026 Himalayas first sighting
Seen on 1 board over 0 days.