Vetted Data Engineers in the NYC Metro

Rom Manzano

Screened

Executive technical founder and full-stack engineer specializing in AI, SaaS, and FinTech

New York, NY15y exp

“Engineer coming out of a venture studio as it winds down, now seeking another zero-to-one environment with strong studio support and go-to-market playbooks. They show a thoughtful founder mindset centered on rapid shipping, design-partner validation, lean execution, and testing whether users will actually pay for a workflow-specific solution.”

“Data/analytics engineer who stood up foundational pipelines and services at Meta for the Ray-Ban Meta launch—building a retailer sales ingestion system (S3/Hive) with rigorous DQ checks, 1-day SLAs, and dimensional rollups used by GTM to track sales trends. Also built a modular multi-retailer web-scraping system for out-of-stock alerts and shipped internal GraphQL APIs and an n8n-like workflow builder using serverless (AWS Lambda) with strong testing and observability practices.”

“Data engineer focused on large-scale ETL/ELT pipelines across cloud stacks (GCP and AWS), including Spark-based transformations and orchestration with Airflow. Has experience loading up to ~2TB per BigQuery target table and designing atomic loads to multiple downstream systems (Elasticsearch + Kafka), with Kubernetes deployment and Jenkins CI/CD.”

“Data engineer with healthcare (CVS Health) experience who migrated production PySpark workloads to native BigQuery SQL and built a Great Expectations-based validation microservice on GKE (Flask + REST) integrated into Cloud Composer. Has operated high-volume pipelines (~300–400GB/day) and designed external vendor ingestion on AWS (Lambda/Step Functions/Glue) with schema-drift detection, alerting, and backfill-safe controls to protect downstream Snowflake/BigQuery tables.”

“Data engineering-focused candidate (internship/project experience) who built end-to-end pipelines processing a few million transactional records/day for fraud detection and reporting, using Airflow, Python/SQL, and PySpark with strong emphasis on data quality gates, idempotency, and monitoring. Also implemented an external web/API data collection system with anti-bot tactics and schema-change quarantine, and shipped a versioned Flask API to serve curated warehouse data.”

Zhiwen Zhao

Screened

Junior Data Engineer specializing in cloud ETL and big data platforms

New York, NY3y exp

“Data engineer focused on transit/transportation datasets, building Spark-based pipelines that ingest from Oracle/APIs, apply PySpark data-quality fixes, and publish star-schema fact tables to Azure Data Lake. Experienced troubleshooting complex Spark failures (using checkpointing to manage long lineage) and operating Airflow-driven backfills and GitLab CI deployments for production DAGs.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”