Note: The job is a remote job and is open to candidates in USA. Hire Hangar Global is seeking a skilled Data Engineer to design, build, and maintain scalable data infrastructure for a fast-growing tech company. The role requires deep technical expertise in data pipelines, warehousing, and architecture, as well as collaboration with data science, product, and engineering teams to deliver reliable data solutions.
Responsibilities
- Design, build, and maintain robust, scalable data pipelines and ETL/ELT workflows
- Develop and manage data warehousing solutions to support business intelligence and analytics needs
- Ensure data quality, integrity, and availability across all data systems and sources
- Collaborate with data scientists, analysts, and product teams to understand and fulfill data requirements
- Optimize query performance and database architecture for speed and efficiency
- Monitor, troubleshoot, and resolve data pipeline failures and incidents
- Implement and enforce data governance, security, and compliance best practices
- Document data models, processes, and infrastructure to support team knowledge sharing
Skills
- 4+ years of experience as a Data Engineer or in a similar data infrastructure role within a tech company (non-negotiable)
- Strong proficiency in SQL and at least one programming language such as Python or Scala
- Hands-on experience building and managing ETL/ELT pipelines using tools such as Apache Airflow, dbt, or similar
- Experience with cloud data platforms such as AWS, GCP, or Azure, and data warehouses such as Snowflake, BigQuery, or Redshift
- Solid understanding of data modeling, schema design, and database optimization
- Strong problem-solving skills with the ability to manage complex, high-volume data environments
- Must have prior remote work experience, be fluent with remote collaboration tools and platforms (such as Slack, Zoom, Google Workspace, Asana, or similar), and have ideally worked with US or UK-based companies. Applications without this experience will not be considered
- Experience with real-time or streaming data pipelines using tools such as Kafka or Spark
- Familiarity with data lakehouse architectures (e.g., Delta Lake, Apache Iceberg)
- Exposure to machine learning workflows and supporting ML data infrastructure
- Experience working in an Agile or DevOps environment with CI/CD practices applied to data workflows
Company Overview