Data Engineer
Job Description
About Huzzle
At Huzzle, we connect exceptional talent with exciting opportunities at leading companies across the UK, US, Canada, Europe, and Australia. We work with a diverse range of clients, including startups, digital agencies, and technology-driven businesses across industries such as SaaS, MarTech, FinTech, and AI.
Unlike traditional outsourcing agencies, we place you directly with our clients, where you become an integral, in-house member of their team.
Job Type: Full-time
Location: Remote
Job Summary
As a Data Engineer, you will play a key role in designing, developing, and maintaining scalable data pipelines and infrastructure. You will collaborate with data analysts, data scientists, software engineers, and business stakeholders to ensure data is reliable, accessible, and of high quality across the organisation.
This is a great opportunity for professionals who enjoy tackling complex data challenges and building robust systems that power business intelligence, analytics, and machine learning initiatives.
Key Responsibilities
- Design, build, and maintain scalable ETL and ELT data pipelines.
- Develop and optimize data architectures, databases, and data warehouses.
- Integrate data from various sources, APIs, and third-party platforms.
- Maintain data quality, accuracy, consistency, reliability, and security.
- Monitor, troubleshoot, and improve data pipelines and workflows.
- Work closely with analytics, engineering, and business teams to identify and meet data requirements.
- Establish data governance standards and best practices for data management.
- Improve data storage, processing speeds, and query performance.
- Support business intelligence, reporting, and analytics projects.
- Maintain documentation of data models, workflows, and technical processes.
- 2+ years of experience in data engineering, data warehousing, or a similar field.
- Strong SQL skills and a solid understanding of database management.
- Proficiency in Python, Java, Scala, or other relevant programming languages.
- Practical experience with ETL/ELT tools and building data pipelines.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform.
- Hands-on experience with modern data warehouse solutions such as Snowflake, Redshift, BigQuery, or Databricks.
- Knowledge of workflow orchestration tools such as Airflow or Prefect.
- Understanding of data modelling, governance, and data quality best practices.
- Experience handling large datasets and working with distributed systems is an advantage.
- Strong analytical thinking, problem-solving, and communication abilities.
- Comfortable working independently while collaborating with a remote team.
