Senior Data Engineer (Python, Databricks)

As a Senior Data Engineer in Enterprise Platforms, you will join an agile team focused on enterprise communication data and building scalable data pipelines on the Palmos platform for regulatory controls. You will design, develop, and maintain secure, reliable, and scalable data solutions that support business controls, analytics, reporting, and AI/ML use cases across the firm. You will collaborate with domain teams, platform engineering, and operations partners to deliver high-quality curated datasets aligned to our data mesh architecture and Palmos platform standards. You will play a key role in advancing our data capabilities and driving innovation., * Develop workflows and ELT data pipelines using Python, Spark/PySpark, and Databricks

• Onboard enterprise datasets into Palmos, including ingestion, transformation, and validation of data assets

• Build, test, and maintain scalable data pipelines and data architectures that support enterprise controls and analytics use cases

• Build business controls algorithms for communications data to identify anomalies

• Build data completeness and integrity controls for the pipelines

• Apply data engineering best practices for performance optimization, reliability, and maintainability

• Use SQL extensively and work with both relational and NoSQL data stores

• Partner with producers to understand data requirements and translate them into production-ready solutions

• Apply SDLC practices including CI/CD, testing, and operational monitoring to ensure pipeline stability

• Contribute to reusable frameworks and standards to accelerate onboarding and pipeline delivery

• Identify data issues, anomalies, and optimization opportunities to improve data quality and performance

• Leverage enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (eg, code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contribute learnings and reusable patterns to improve broader team effectiveness

• Hands-on experience with Databricks, Spark/PySpark, Python, and SQL

• Experience developing and maintaining data pipelines and data processing systems

• Understanding of the data life cycle, including ingestion, transformation, storage, and consumption

• Knowledge of cloud platforms (AWS) and distributed data processing

• Experience with SDLC practices including CI/CD, testing, and deployment

• Strong problem-solving skills and ability to troubleshoot data and pipeline issues

• Ability to collaborate effectively within agile teams and across stakeholders

• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (eg, for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security

• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices

Preferred Qualifications, Capabilities, and Skills:

• Experience with Databricks lakehouse, Databricks Genie, Delta Lake, and medallion architecture

• Familiarity with enterprise data platforms and data mesh principles

• Exposure to data quality, observability, and metadata management tools

• Experience supporting analytics, reporting, or AI/ML workloads

• Experience working on regulatory controls