The Senior Data Engineer builds and runs the enterprise data products MeridianLink uses to run the business, including master data (MDM), financial and revenue datasets, AI usage reporting, and governed self-service reporting in Sigma. You’ll work on Databricks with Informatica-ingested data from Salesforce, NetSuite and our product platforms. You’ll partner with Finance, Product and the platform data teams to turn business rules into tested, documented pipelines.
Expected Duties:
Design, build and maintain batch and incremental pipelines on Databricks (Spark, SQL, Python, Spark Declarative Pipelines) through the medallion layers, including SCD2 history and reconciliation.
Own and extend MDM matching: cross-reference tables, human review of matches, and master ID assignment. Accuracy comes before automation.
Build financial datasets (billing, revenue, ARR inputs) with cross-functional teams and walking them through how the numbers are built.
Build and Maintain Documentation
Ship data pipelines through CI/CD (GitLab, Databricks Asset Bundles) with automated tests, schema checks, validation and rollback plans.
Understand Unity Catalog governance: least-privilege sharing, role-based PII masking, and lineage.
Join the on-call rotation and write runbooks and documentation others can use.
Use AI-assisted engineering tools and evaluate new technology through POCs.
Reverse Engineer pipelines or code to upgrade or modernize
Qualifications: Knowledge, Skills, and Abilities:
Required
4-6 years experience in data engineering.
Deep Knowledge in SQL, Python, Spark and Databricks.
Experience with Salesforce and/or NetSuite data.
Experience handling PII (masking and removal).
Comfortable working independently and explaining data to non-technical stakeholders.
Preferred
AI-SDLC knowledge.
Best practices when using AI to assist in development.
Experience democratizing data for business users.
Educational Qualifications:
Bachelor’s or master’s degree in Computer Science, Engineering, or a related field.
Skills Required
- Bachelor's or master's degree in computer science, engineering, or a related field
- 6-8 years of experience in data engineering, focused on financial systems on SaaS platforms
- Deep expertise in Python and SQL
- Experience with distributed processing frameworks such as Apache Spark and Databricks
- Proven experience with cloud-based data platforms, preferably AWS or Azure
- Hands-on experience with data orchestration tools such as Airflow or dbt
- Experience with data warehouses such as Databricks, Snowflake, Redshift, or BigQuery
- Strong understanding of data security, privacy, and compliance in financial services
- Experience working with structured and semi-structured data formats including Delta, JSON, Parquet, and Avro at scale
- Familiarity with modeling datasets in Salesforce, NetSuite, and Anaplan
- Experience democratizing data at enterprise scale
- Experience improving enterprise processes and reducing costs through data
What We Do
Pioneering Technologies for Your Financial Institution Since 1998, we have been creating innovative technologies that transform the way financial institutions operate by solving complex problems with streamlined, user-friendly solutions. Our robust and secure technologies empower lenders and consumers to get reliable, accurate information every time, at any time. As well-established industry leaders, we continue to set the industry standard for web-based credit reporting and lending for financial institutions of every size.
