About the role
As an Data Engineer, you will be responsible for designing, developing, and maintaining data infrastructure and analytics solutions to support data-driven decision-making and reporting. You will collaborate with other internal teams such as Business Intelligence, Data Operations, IT, and other stakeholders to understand business requirements and translate them into technical solutions. Your role will potentially involve working with large and complex data sets, implementing data pipelines, and ensuring data accuracy, integrity, and availability.
Responsibilities:
- Develop and maintain data infrastructure: Design, build, and optimize data pipelines, ETL/ELT processes, and data warehouses to support efficient data collection, storage, and retrieval.
- Data modeling and schema design: Define data models and schemas to enable efficient data analysis, reporting, and visualization. Ensure adherence to data governance and data quality standards.
- Data transformation and manipulation: Cleanse, transform, and aggregate data to derive meaningful insights. Identify data inconsistencies, outliers, and anomalies and implement appropriate solutions.
- Implement data analytics solutions: Collaborate with different company departments to understand their requirements and develop analytics solutions that meet their needs.
- Documentation and communication: Document data processes, data flows, and data structures. Clearly communicate technical concepts and findings to non-technical stakeholders such as Credit/Structuring, Operations, Legal, etc.
Requirements
Qualifications:
- Bachelor's degree in Computer Science, Information Systems, Data Science/Analytics, or a related field.
- 4+ years of experience as an Data Engineer, Analytical Engineer, Data Analyst, Business Intelligence Engineer, or similar role
- Experience working with finance-related data sets (ex: FinTech, Lending, Financial Services, or related industries)
- Advanced SQL skills and experience working with complex data sets.
- Understanding of the following is advantageous:
oData Modeling
oData Governance
oETL/ELT practices
oData transformations with dbt
oData warehousing with Snowflake
oCloud computing with AWS
oData orchestration tools (e.g. Airflow, Dagster)
oAPI Usage and Integration - Strong analytical and problem-solving skills, with the ability to transform complex data into actionable insights.
- Excellent communication and collaboration skills, with the ability to interact effectively with both technical and non-technical stakeholders.
- Strong attention to detail and a commitment to delivering high-quality work within deadlines.
- Willingness to stay updated with emerging technologies, tools, and best practices in data engineering and analytics. Ability to adapt to evolving business needs and work in a fast-paced environment.