Staff Data Engineer

Founded by experienced entrepreneurs and engineers in 2016, Pismo is a technology company that provides a comprehensive processing platform for banking, card issuing and financial market infrastructure and helps customers innovate and build the next generation of banking and payment solutions. Pismo joined Visa in 2024.

Leveraging Visa’s solutions, our core platform, and an expanding suite of capabilities, Pismo addresses the technological challenges that large banks, marketplaces, and fintech companies face in migrating from legacy systems to more advanced technology in the market. Pismo’s cloud-based platform empowers firms to build and launch financial products rapidly, scaling as they grow to have a broader audience while keeping high security and availability standards.

Pismo’s 500+ employees are located in more than 10 countries around the world.

We’re hiring a Staff Data Engineer to design and build robust data pipelines for our corporate Data Lake. In this role, you’ll own data products end-to-end in production, work autonomously on significant projects, and mentor junior engineers. You’ll be expected to make sound data engineering trade-offs under limited supervision, with strong hands-on experience in Spark, Databricks, Delta Lake, and Airflow.

At Pismo, the Data Lake team is responsible for centralizing and organizing data into a single, trusted platform that supports decision-making across the company and for external clients. We work on challenges such as scaling global data infrastructure, delivering high-quality reporting, and enabling secure, self-service access to data—helping teams move faster while avoiding information silos.

• Design and implement data ingestion and transformation pipelines (batch and near-real-time) using PySpark/SparkSQL on Databricks.
• Own data pipelines end-to-end in production: freshness, correctness, availability, and SLA adherence.
• Build and maintain Delta Lake tables following medallion architecture patterns (bronze/silver/gold).
• Design and optimize Airflow DAGs (MWAA) for complex orchestration scenarios.
• Implement and maintain data quality frameworks (Great Expectations or equivalent) as integrated pipeline gates.
• Write advanced SQL for data modeling, transformation, and performance optimization.
• Conduct thorough code reviews. mentor Analyst-level engineers through pairing and design guidance.
• Investigate, diagnose, and resolve data quality incidents and pipeline failures independently.
• Collaborate with Analytics, BI, and Product teams to design consumer-friendly datasets.
• Contribute to CI/CD, testing standards, and data governance practices.

This is a remote position. A remote position does not require job duties be performed within proximity of a Visa office location. Remote positions may be required to be present at a Visa office with scheduled notice. #LI-Remote

• Apache Spark (PySpark, SparkSQL) — production experience
• Databricks (jobs, workflows, cluster management, tuning)
• Delta Lake (ACID tables, OPTIMIZE, VACUUM, schema evolution, MERGE)
• Advanced SQL (window functions, CTEs, query optimization)
• Apache Airflow / MWAA (DAG design, retries, backfills, SLAs)
• Amazon S3 data lake design (partitioning, layout, lifecycle)
• Data quality frameworks (Great Expectations or equivalent)
• Data modeling (dimensional / Kimball, medallion layers)
• Git/GitHub, CI/CD for data pipelines
• Terraform
• Python for automation and data processing
• English level B2
• Be based in Brazil

Desirable qualifications:

• CDC patterns (DMS, incremental processing, MERGE upserts) 
• Streaming ingestion (Structured Streaming, Auto Loader) 
• AWS Glue Catalog / Unity Catalog 
• Metadata management (OpenMetadata) 
• BI integration (Superset, dashboarding) 

Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.