Data Engineer, Notifications
Builds and owns scalable data pipelines and models for high-volume notifications, enabling analytics, ML, and operations. Requires 5+ years experience with SQL/Python, modern data tools (Kafka, dbt, Spark), and cloud warehouses.
Responsibilities
- Own data architecture end-to-end: define capture, model, and serve critical business data; implement in production with decisions on storage, compute, SLAs.
- Build mission-critical pipelines: develop batch workflows for high-volume notification events with guarantees on latency, completeness, accuracy.
- Design canonical models: create domain-oriented data models for analytics, ML, production apps; enforce standards, ownership, data contracts.
- Enforce data quality: build tests, lineage, monitoring, reconciliation for observability and anomaly detection.
- Automate workflows: partner to eliminate manual handoffs, reconcile data across services, warehouses, external systems.
- Enable insights: support analytics, ML, product teams with high-quality, self-healing data assets.
Requirements
- 5+ years in data or software engineering.
- Strong experience with production-grade data pipelines, SLAs, monitoring, alerting.
- Deep SQL expertise: complex models, dependencies, optimization.
- Production code in Python or SQL; CI/CD, infrastructure-as-code.
- Hands-on with ingestion (Kafka, Debezium), transformation (dbt, Spark, Flink), orchestration (Dagster, Airflow), observability (Monte Carlo, Great Expectations).
- Operate cloud warehouses (Snowflake, BigQuery, Redshift): schema, cost, tuning.
- Work with large-scale datasets (hundreds of millions rows/day).
- Design models for analytics, ML, operational use.
- Systems thinking: correctness, latency, cost, maintainability.
- Self-starter, autonomous; strong communication.
Compensation
$195,000 - $230,000/year base salary + equity + benefits (health, 401k match, parental leave, etc.).
Staff Data Platform Engineer
Staff Data Platform Engineer building and leading AWS-native data platform architecture, orchestration, governance, and AI-readiness for analytics and ML workloads. Requires 8-10+ years experience with AWS data systems and strong technical leadership.
Manager, Data Engineering
Lead and mentor a team of data engineers building scalable data pipelines and platform infrastructure. Hands-on coding, operational excellence, and cross-functional collaboration with analytics, data science, and business teams.
Senior Data Engineer, People Analytics
Build and maintain data pipelines, tables, and AI-ready data foundations from HR systems to power People Analytics reporting, dashboards, and LLM tools. Requires 5+ years of data engineering experience with strong SQL, Python, Airflow, and data governance skills.