MeridianLink
Data Engineer
US Remote
Sponsorship not specifiedDetected 29 days ago
PythonFlaskCode ReviewGitSQLDatabricksAzureCI/CDJenkinsSparkData EngineeringTest AutomationUnityCommunication
About the role
- The successful candidate will apply modern software engineering practices, including AI-assisted development tools, to improve productivity, code quality, and delivery speed while maintaining strong engineering standards.
Responsibilities
- Design, develop, and maintain scalable data pipelines and data products for
- Build and optimize batch and near real-time data ingestion, transformation, and
- Integrate data from internal and external sources to support business, reporting,
- Collaborate with data architects, analysts, data scientists, and business
- stakeholders to deliver scalable data solutions and support Sisense dashboards
- Design and implement data models that support reporting, analytics, and
- Support CI/CD, infrastructure automation, technical documentation, and
Requirements
- 2-4 years of professional experience in Data Engineering, Data Warehousing, or
- Strong hands-on experience with Python and SQL for building scalable data
- Experience with Apache Spark, Parquet, and Azure Databricks, including
- Strong SQL expertise including performance tuning, indexing, partitioning, query
- Experience supporting or working with BI tools such as Sisense (or similar
- Experience with CI/CD pipelines and version control practices (e.g., GitLab,
- Experience working in fast-paced product environments with an emphasis on
- Solid understanding of ETL/ELT methodologies, data warehousing principles,
- Experience designing and implementing data models to support analytics,
- Strong communication skills with the ability to collaborate across technical and
Nice to have
- Ability to navigate ambiguity, prioritize effectively, and adapt to changing
- Prior experience in financial services or regulated environments is a plus
- the following frameworks such as Streamlit, Dash, Flask, Gradio, Shiny, or
Skills
- They will ensure efficient and reliable data delivery across multiple teams, systems, and products in a dynamic environment.
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