Databricks Data Engineer

CGI•Atlanta, GA

1d•Remote

About The Position

Are you fascinated by information technology and its role in innovative business solutions? Are you a collaborative problem solver who wants to build a dynamic career making an impact for some of the most influential companies and government agencies in the world? If so, we think CGI is just the place for you. As a Databricks Data Engineer on our team, you'll work in a highly collaborative environment to provide expertise in designing, developing, and executing solutions to enhance the quality of IT products and services. At CGI, you can explore your full potential – not confined by borders or pre defined paths. You're empowered to solve problems in your own unique way which is not only valued and respected but encouraged. We're a close knit team that has access to global resources. You'll have the opportunity to explore a wide range of tools, technologies, and cutting edge solutions, all while enjoying the personal touch that our local operating approach offers. This position can be performed remotely from anywhere in the United States. Preference will be given to candidates located in or near Atlanta, Georgia, or within commuting distance of a CGI office.

Requirements

5+ years of Data Engineering, ETL/Data Warehouse, or Big Data experience, including 3+ years of hands on Azure Databricks development.
Strong hands on development experience with Databricks, PySpark, Python, Spark SQL, SQL, Delta Lake, and ADLS.
Experience designing and developing scalable ETL/ELT pipelines for large volume transactional and financial datasets.
Strong experience with data transformation, source to target mapping, cleansing, reconciliation, data quality, and exception handling.
Credit card/payments domain experience with knowledge of card accounts, transactions, authorizations, payments, balances, billing, and settlement.
Experience with Azure DevOps, Git, CI/CD, and Agile/Scrum development practices.
Strong banking/financial services experience and understanding of financial data controls and reconciliation.
Bachelor's degree in Computer Science, Engineering, Information Technology, Data Science, or a related field.

Nice To Haves

Experience with TSYS or another major credit card processing platform.
Experience supporting credit card conversions, migrations, or platform modernization.
Knowledge of the end to end card lifecycle, including authorization, authentication, clearing, settlement, posting, and reconciliation.
Experience with Mastercard and/or Visa data, interfaces, reporting, rewards, or loyalty programs.
Understanding of PCI DSS, PAN protection, tokenization/de tokenization, encryption, and sensitive cardholder data management.
Experience with advanced Databricks capabilities such as Unity Catalog, Workflows, Auto Loader, and Lakeflow/Delta Live Tables.
Experience modernizing Informatica PowerCenter or SSIS workloads to Databricks/PySpark.
Azure and/or Databricks certifications and experience with large scale banking conversion initiatives.

Responsibilities

Design, develop, and maintain Azure Databricks pipelines for credit card data ingestion, transformation, integration, and downstream consumption.
Develop scalable processing solutions using PySpark, Python, Spark SQL, SQL, Delta Lake, and ADLS for high volume card datasets.
Build transformation logic for cardholder, account, transaction, authorization, payment, balance, fee, interest, rewards, and servicing data.
Translate business requirements and source to target mappings into reusable Databricks notebooks, workflows, jobs, and ETL/ELT processes.
Support credit card platform conversion and modernization, including data mapping, historical/incremental loads, transformation, and reconciliation.
Implement automated data quality, balancing, reconciliation, cleansing, and exception handling controls across source and target environments.
Protect sensitive cardholder information, including PAN and PCI sensitive data, through appropriate masking, tokenization, encryption, and data handling practices.
Troubleshoot and optimize Databricks pipelines while collaborating with credit card SMEs, architects, analysts, and application teams.