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Data Engineer at MeridianLink. Remote Location: US Remote. We are seeking an accomplished Data Engineer to join our rapidly growing team. This role is . responsible for designing, building, and evolving scalable data pipeline architecture to . ensure reliable, high-quality data delivery across the organization.. The ideal candidate is a hands-on engineer with strong experience building and . maintaining data pipelines, and a passion for delivering robust data solutions that enable . analytics and business decision-making.. The Data Engineer will partner with data architects, data analysts, data scientists, and . cross-functional stakeholders to deliver trusted data assets supporting a wide range of . business initiatives. They will ensure efficient and reliable data delivery across multiple . teams, systems, and products in a dynamic environment.. This role offers the opportunity to evolve and enhance a modern data platform by improving . existing pipelines or redesigning them for greater scalability, performance, and . maintainability. 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 . internal and external consumers.. • Build and optimize batch and near real-time data ingestion, transformation, and . delivery processes.. • Integrate data from internal and external sources to support business, reporting, . and analytics requirements.. • Collaborate with data architects, analysts, data scientists, and business . stakeholders to deliver scalable data solutions and support Sisense dashboards . and analytics assets.. • Design and implement data models that support reporting, analytics, and . operational use cases.. • Ensure data quality, reliability, and performance through monitoring, validation, . automated testing, and troubleshooting.. • Write maintainable, well-documented, and testable code; participate in code . reviews; and leverage AI-assisted development tools to improve quality and . efficiency.. • Support CI/CD, infrastructure automation, technical documentation, and . continuous improvements to data architecture, tooling, and engineering practices. QUALIFICATIONS. • 2–4 years of professional experience in Data Engineering, Data Warehousing, or . related roles.. • Strong hands-on experience with Python and SQL for building scalable data . pipelines and transformation logic.. • Experience with Apache Spark, Parquet, and Azure Databricks, including . Databricks workflows, Delta Lake, Delta Sharing, and Unity Catalog.. • Strong SQL expertise including performance tuning, indexing, partitioning, query . optimization, and stored procedure development.. • Solid understanding of ETL/ELT methodologies, data warehousing principles, . and modern data engineering best practices.. • Experience designing and implementing data models to support analytics, . reporting, and operational use cases.. • Experience supporting or working with BI tools such as Sisense (or similar . platforms).. • Experience with CI/CD pipelines and version control practices (e.g., GitLab, . Jenkins, or equivalent).. • Experience working in fast-paced product environments with an emphasis on . delivery, maintainability, and minimizing technical debt.. • Strong communication skills with the ability to collaborate across technical and . non-technical stakeholders. BONUS QUALIFICATIONS. • Experience building lightweight data applications or internal tools using any of . the following frameworks such as Streamlit, Dash, Flask, Gradio, Shiny, or . Node.js.. • Ability to navigate ambiguity, prioritize effectively, and adapt to changing . business needs.. • Prior experience in financial services or regulated environments is a plus