Key Responsibilities

Design, develop, and maintain scalable ETL/ELT data pipelines.
Build and optimize data ingestion, transformation, and processing workflows.
Work with large structured and unstructured datasets from multiple data sources.
Develop and maintain data models supporting analytics and reporting requirements.
Ensure data quality, integrity, consistency, and availability across platforms.
Develop solutions using SQL and programming languages such as Python.
Work with cloud-based data platforms and services.
Optimize queries, pipelines, and data processing jobs for performance and scalability.
Implement data validation, monitoring, and error-handling processes.
Collaborate with Data Analysts and Data Scientists to understand data requirements and deliver appropriate datasets.
Support data warehouse and data lake architectures.
Participate in technical design discussions, code reviews, and architecture decisions.
Troubleshoot and resolve data pipeline and production issues.
Follow data governance, security, and compliance standards, particularly when working with sensitive financial information.
Create and maintain technical documentation for data pipelines, models, and processes.
Mentor junior engineers and contribute to data engineering best practices.

Required Qualifications

5–8 years of professional experience in Data Engineering, Data Development, or related roles.
Strong hands-on experience with SQL.
Strong programming experience with Python.
Experience designing and developing ETL/ELT pipelines.
Experience working with large-scale data processing and transformation.
Strong understanding of data modeling, data warehousing, and data lake concepts.
Experience with relational and/or NoSQL databases.
Experience with at least one major cloud platform such as AWS, Azure, or GCP.
Knowledge of cloud-based data storage and processing technologies.
Experience with Git and CI/CD development practices.
Strong analytical, troubleshooting, and problem-solving skills.
Experience working within Agile development environments.

Experience with technologies such as Apache Spark, Databricks, Snowflake, Kafka, Airflow, or similar data platforms.
Experience with cloud data services such as AWS Glue, Redshift, S3, Azure Data Factory, Synapse, ADLS, BigQuery, or Dataflow.
Knowledge of distributed data processing architectures.
Experience with real-time or streaming data pipelines.
Familiarity with data governance, lineage, and data quality frameworks.
Experience within banking, financial services, lending, or consumer finance environments is a plus.
Understanding of financial data security and regulatory requirements is desirable