Data Engineer

Silversmith Capital Partners; United States; On-site; USD 85,000 - 110,000; Full time

Job summary

MeridianLink is seeking a Data Engineer to design and build scalable data pipeline architecture. This role collaborates closely with data architects and analysts to ensure reliable data delivery and support business initiatives.

The successful candidate will have strong experience with Python, SQL, and tools like Apache Spark and Azure Databricks. This opportunity allows the individual to enhance a modern data platform while leveraging best practices in software engineering.

Qualifications

• 2–4 years of professional experience in Data Engineering or related roles.
• Strong hands-on experience with Python and SQL for data pipelines.
• Experience with Apache Spark and Azure Databricks.

Responsibilities

• Design and maintain scalable data pipelines and data products.
• Collaborate with stakeholders to deliver scalable data solutions.
• Ensure data quality and performance through validation and monitoring.

Skills

Python

SQL

Apache Spark

Data Engineering

ETL/ELT

Tools

Azure Databricks

GitLab

Sisense

Job description

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-pace 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.