Sr Data Engineer - MeridianLink

The Senior Data Engineer will design, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading (ETL) of data from a variety of data sources. The role will develop robust and scalable solutions that transform data into a useful format for analysis, enhance data flow, and enable end-users to consume and analyze data faster and easier.

Expected Duties

• Design, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading (ETL) of data from diverse data sources.
• Lead the writing of complex SQL queries to support analytics needs.
• Develop technical tools and programming leveraging AI, machine learning, and big-data techniques to cleanse, organize, and transform data while maintaining data structures and integrity automatically.
• Evaluate and recommend tools and technologies for data infrastructure and processing.
• Collaborate with engineers, data scientists, data analysts, product teams, and stakeholders to translate business requirements into technical specifications and coded data pipelines.
• Work with modern frameworks and languages such as R, Python, SQL, Databricks, Spark, Delta, and APIs across structured and unstructured data stores (data lakes, RDBMS, data warehouses).
• Architect and scale our modern data platform to support real-time and batch processing for financial forecasting, risk analytics, and customer insights.
• Enforce high standards for data governance, quality, lineage, and compliance.
• Partner with stakeholders across engineering, finance, sales, and compliance to translate business requirements into reliable data models and workflows.
• Evaluate emerging technologies and lead POCs that shape the future of our data stack.
• Champion a culture of security, automation, and continuous delivery in all data workflows.

Technical Qualifications

• Deep expertise in Python, SQL, and distributed processing frameworks like Apache Spark, Databricks, Snowflake, Redshift, and BigQuery.
• Proven experience with cloud-based data platforms (preferably AWS or Azure).
• Hands-on experience with data orchestration tools (e.g., Airflow, dbt) and data warehouses.
• Strong understanding of data security, privacy, and compliance within a financial services context.
• Experience working with structured and semi-structured data (e.g., Delta, JSON, Parquet, Avro) at scale.
• Familiarity with modelling datasets in Salesforce, Netsuite, and Anaplan.
• Previous experience democratizing data at scale for the enterprise.

Educational and Work Experience

• Bachelor's or Master's degree in computer science, Engineering, or a related field.
• 6-8 years of experience in data engineering, with a strong focus on financial systems on SaaS platforms.