Sr Data Engineer
MeridianLink
•$95,000 - $135,000
About The Position
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.
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 modelling datasets in Salesforce, Netsuite and Anaplan to solve business use cases required.
- 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.
Nice To Haves
- Work on problems of diverse scopes where analysis of information requires evaluation of identifiable factors.
- Work is expected to be done independently through independent judgment.
- Ability to assess unusual circumstances and use sophisticated analytical and problem-solving techniques to identify the cause.
- Working with people, processes facing Enterprise a plus, improvement of processes/reduction of costs in the enterprise processes leveraging data is preferable.
- Ability to enhance relationships and networks with senior internal partners who are not familiar with the subject often requires persuasion.
- Previous experience Democratizing data at scale for the enterprise a huge plus.
Responsibilities
- Design, build, implement, and maintain data processing pipelines for the extraction, transformation, and loading (ETL) of data from a variety of data sources.
- Lead the writing of complex SQL queries to support analytics needs.
- Develop technical tools and programming that leverage artificial intelligence, machine learning, and big-data techniques to cleanse, organize and transform data and to 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.
- Work with structured and unstructured data from a variety of data stores, such as data lakes, relational database management systems, and/or 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
