Senior Data Engineer, BR-DGE

• Proven experience designing, building, and operating production grade data pipelines and platforms
• Strong SQL, specifically PostgreSQL, plus at least one programming language such as Python or Java
• Experience with data processing or orchestration tooling such as Spark, Airflow, or Kafka
• Experience designing data models for analytics and reporting workloads
• Practical knowledge of data quality, testing, observability, lineage, and governance patterns
• Strong experience with AWS based data platforms, with hands on use of services like S3, Glue, Athena, Redshift, Kinesis, EMR, or MSK
• Infrastructure as code experience using Terraform or CloudFormation, and comfort operating systems in production
• Ability to collaborate across Engineering, Product, Analytics, and Data Science, and drive standards through influence

Desirable

• Data is becoming a critical part of BR DGE’s next growth phase, powering internal analytics and customer facing insights and monitoring
• The data engineering space is largely greenfield. We need a production grade data platform that can ingest, transform, validate, and monitor data from core systems and operational tooling
• The robustness, scalability, and governance of our data architecture impacts our ability to grow safely and meet regulatory expectations
• This role owns the insights data platform, while partnering closely with Analytics, Product, and Engineering to ensure the platform delivers trusted datasets and timely signals
• Design and ship a tiered data platform that supports multiple latency needs, including low latency pipelines for operational monitoring and customer facing insights, plus batch pipelines for reporting and deeper analysis
• Build and own end-to-end ingestion patterns across batch, micro batch, and selected near real time use cases, with strong orchestration and dependency management
• Implement schema evolution, data contracts, and approaches for late arriving and corrected data so consumers can trust the outputs
• Treat curated datasets as products that are well defined, documented, reliable, and safe to use for both internal and external consumers
• Set platform standards for idempotent ingestion, deduplication, data quality, lineage, and observability
• Ensure the platform meets regulated fintech & payments expectations for access control, security, and governance while staying cost efficient as volumes grow
• Partner with Product and Engineering on event and domain modelling. Decide what data gets emitted and what latency and granularity is needed for analytics and product goals
• Support Data Science with reliable feature ready datasets and pragmatic collaboration, without owning reporting or business analysis
• Evolve the current lightweight tooling into a more observable, structured platform. Improve standards without creating unnecessary platform complexity
• Automate data infrastructure and workflows using infrastructure as code and CI CD practices