Testing: unit/integration tests for transformations, data quality checks, contract testing, and replay/backfill procedures.
Version control & code review discipline; clear documentation and runbooks
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 5+ years hands on Software Development Life Cycle experience
Experience in data engineering experience building and maintaining data pipelines.
Hands-on experience building and operating a Databricks Lakehouse Hosted in AWS
Experience with Delta Lake (ACID tables, partitioning, schema evolution,
Proven experience with Spark on Databricks (performance tuning, cluster sizing, skew mitigation, joins, caching, file sizing).
Experience with streaming and batch pipelines (Structured Streaming; incremental processing; backfills; late-arriving data).
Strong AWS fundamentals for data platforms: S3, IAM, KMS, networking basics (VPC/security groups), logging/auditing.
Experience implementing data governance/security controls in Databricks (e.g., Unity Catalog, table/column permissions, credential passthrough patterns as applicable)
Demonstrate experience with reliability: monitoring/alerting, incident response, RCA, and SLO/SLA management
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.
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Preferred qualifications, capabilities, and skills
Familiarity with modern front-end technologies
Exposure to cloud technologies
