Staff Infrastructure Engineer, Zocdoc

• Motivated by availability, latency, and cost SLOs for data platforms, owning them end‑to‑end
• Equally comfortable designing multi‑account AWS architectures and hands‑on debugging of Spark jobs
• Pragmatic: you favor simple, operable solutions and automation over bespoke one‑offs
• Excited to influence data producers/consumers via contracts, governance, and self‑service platforms
• 8+ years in infrastructure/SRE/platform engineering (or hybrid data platform) with deep AWS expertise (networking, IAM, CDK, S3, EKS, EMR, Glue, Lambda; multi‑account patterns)
• Expertise running distributed data processing at scale with Spark (Databricks or EMR/EKS), plus working knowledge of Hadoop/Hive/Presto/Trino
• Strong SQL fundamentals and data modeling; Snowflake (or BigQuery/Redshift) performance and cost optimization
• Proven leadership establishing SLOs, runbooks, incident response, and reliability tooling for data platforms
• Solid IaC (Terraform), CI/CD, and security‑by‑default mindset for PHI/PII

Desirable

• We’re hiring a Staff Infrastructure Engineer (Data) to design, harden, and operate the distributed systems that power analytics, experimentation, and ML at Zocdoc
• You’ll set the long‑term architectural vision for our AWS‑based data platform, establish SLOs and guardrails, and drive cost, reliability, and security for data at scale
• This is an infrastructure‑heavy role (SRE/platform engineering mindset) for someone who loves building robust, stable, and governed data foundations, not just pipelines
• Own platform reliability & operations for data infrastructure: capacity planning, incident response/on‑call, disaster recovery, and performance tuning across compute and storage (e.g., Databricks, Snowflake, Glue/Athena, Kinesis/Kafka)
• Architect the lake / lakehouse layers (Delta Lake / Apache Iceberg / Hudi) with clear data contracts, schema evolution, compaction, and retention; build the controls that make it safe and fast
• Harden platform surfaces (networking, IAM, encryption, key management, VPC endpoints/PrivateLink, Lake Formation/Unity Catalog) for PHI/PII, auditing, and least‑privilege by default
• Establish observability for data systems (Datadog/CloudWatch): golden signals, lineage, SLIs/SLOs, and cost telemetry/FinOps
• Infrastructure‑as‑Code everything (Terraform/CDK) including EMR/EKS clusters, Snowflake roles/warehouses, secrets, and CI/CD for data platform changes
• Optimize warehouse usage (Snowflake preferred; BigQuery/Redshift welcome): warehouse sizing/queuing, clustering, pruning, caching, RBAC, cost controls
• Partner with data engineering & security to set platform standards (orchestration like Dagster/Airflow; governance via Unity Catalog/Collibra/Lake Formation; quality and metadata services)