Data Engineer

Responsibilities

• Own data architecture end-to-end, defining capture, modeling, and serving of critical business data, implementing in production with decisions on storage, compute, and SLAs.
• Build mission-critical streaming and batch data pipelines for high-volume events across user activity, transactions, experimentation, marketing, and telemetry.
• Design and implement canonical domain-oriented data models as source of truth for analytics, ML, and real-time applications; enforce standards, ownership, and contracts.
• Enforce data quality at scale with tests, lineage, monitoring, and reconciliation systems.
• Automate operational workflows, partnering with business systems and platform teams to eliminate manual handoffs.
• Enable insights and experimentation by exposing high-quality data through semantic layers, APIs, and real-time query systems.

Requirements

• 3+ years experience as data or software engineer building data warehouses, distributed systems, or event-driven architectures.
• Design and implement data models using dimensional, Data Vault, or ledger-style techniques.
• Hands-on expertise with ingestion (Kafka, Debezium), transformation (dbt, Spark, Flink), orchestration (Dagster, Airflow), observability (Monte Carlo, Great Expectations).
• Operate cloud data warehouses like Snowflake, BigQuery, Redshift (schema design, cost optimization, workload tuning).
• Write production-grade code in Python or SQL, integrate with CI/CD and infrastructure-as-code.
• Partner across engineering, product, analytics; thrive as self-starter in fast-moving environment.

Compensation

$180,000 - $260,000/year base salary + benefits + equity.