Data Platform Engineer, Abnormal Security

• Pragmatic builder who writes clear SQL/Python and leaves systems more reliable than you found them
• Infrastructure-minded engineer comfortable with Python, IaC, orchestration, and Snowflake administration
• Customer-centric and fundamentals-first; you translate reliability into a delightful data consumer experience
• Velocity-oriented: you deliver “good today” increments, measure impact, and iterate toward excellence
• Owner mindset: you proactively drive outcomes, communicate trade-offs, and follow through on commitments
• Intellectually honest: you share clear, candid updates and invite feedback to improve systems
• Security-first with sound judgment around PII/PHI, least privilege, and secret management
• Collaborative partner who can explain technical topics to both engineers and non-technical stakeholders
• Naturally curious and thrive in ambiguity, seeking to solve business problems with pragmatic solutions
• A self-starter who takes ownership of outcomes and iterates quickly to add value fast
• Always balancing excellence with velocity—knowing when good enough today beats perfect next week
• 3–5+ years of data engineering with strong Python and SQL; hands-on Spark/PySpark (ideally via AWS Glue)
• Deep experience in AWS (S3, IAM, Lambda, CloudWatch) running secure, observable data workloads
• Proficiency operating Snowflake (warehouse sizing, RBAC, resource monitors, clustering/partitioning)
• Proven governance/security patterns: masking policies, row-level security, and auditability
• Orchestration experience (Airflow/MWAA) and event/file/API ingestion beyond managed connectors
• CI/CD for data with GitHub Actions; test/promotion workflows; secrets and PII handling
• Solid grasp of Medallion architecture, dimensional modeling (star schema), and data quality frameworks
• Ownership of incident management and RCA with measurable reduction in MTTR

Desirable

• You’ll build, operate, and improve the “engine room” of our data platform—reusable ingestion frameworks, reliability systems, and pipelines across structured, semi-structured, and unstructured sources
• You’ll own ETL/ELT workflows (Fivetran and AWS Glue) and develop stable, observable, and cost-efficient pipelines that power analytics and AI
• Your work prevents incidents before they happen through great design, guardrails, and monitoring—translating technical reliability into a seamless experience for downstream customers
• Develop reusable ingestion frameworks (Python/Airflow/AWS Glue) for APIs and unstructured sources beyond Fivetran, handling various data formats (JSON, Parquet, etc.)
• Own the end-to-end Medallion (bronze/silver/gold) architecture for core domains, ensuring robust lineage and metadata across diverse data sources
• Implement data observability (native tests, alerts, lineage hooks); lead incident management and root-cause analysis (RCA) for data
• Help standardize reusable “paved-road” patterns (e.g. CI templates, ingestion operators) to improve developer productivity
• Prepare datasets for AI/LLM use cases (feature stores, embeddings/RAG prep)