[Hiring] Senior Data Engineer @MeridianLink

Role Description

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.

• Design, build, implement, and maintain data processing pipelines for ETL of data from various 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.
• Maintain, defend, and update data structures and integrity on an automated basis.
• Evaluate and recommend tools and technologies for data infrastructure and processing.
• Collaborate with engineers, data scientists, data analysts, product teams, and other stakeholders to translate business requirements to technical specifications and coded data pipelines.
• Work with tools, languages, data processing frameworks, and databases such as R, Python, SQL, Databricks, Spark, Delta, APIs.
• Handle structured and unstructured data from data lakes, relational database management systems, and/or data warehouses.

Qualifications

• Ability to work independently and apply judgment to diverse problems.
• Capability to assess unusual circumstances and use analytical techniques to identify causes.
• Experience in improving processes and reducing costs in enterprise processes leveraging data.
• Ability to enhance relationships and networks with senior internal partners.
• Architect and scale modern data platforms for real-time and batch processing.
• 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 the data stack.
• Champion a culture of security, automation, and continuous delivery in all data workflows.

Requirements

• Deep expertise in Python, SQL, and distributed processing frameworks like Apache Spark, Databricks, Snowflake, Redshift, 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 (e.g., Databricks, Snowflake, Redshift, BigQuery).
• 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 modeling datasets in Salesforce, Netsuite, and Anaplan to solve business use cases.
• Previous experience democratizing data at scale for the enterprise is a huge plus.

Educational Qualifications 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.