Data Engineer

Nir Yu; United States; Remote; USD 140,000 - 210,000; Full time

Job summary

Nir Yu is seeking a senior data engineer to own the data platform from architecture to production, enabling concurrent intake from APIs, webhooks, files, and scraping. You will evolve current serial workflows into an event-driven, orchestrated platform with robust recovery and data lineage.

Responsibilities include building multi-source ingestion, organizing medallion data layers, and ensuring data quality with automated tests. Strong SQL, Python, and experience with dbt and GCP are required.

Qualifications

• Experience building multi-source production data platforms from zero to one, incl. architecture and deployment.
• Production experience with workflow orchestration tools (Airflow, Dagster, Prefect, Temporal) incl. retries and failure recovery.
• Hands-on production experience with dbt and Google Cloud Platform (GCP).
• Experience building event-driven systems with messaging or task queues and asynchronous processing.
• Strong SQL and PostgreSQL skills including data modeling and incremental loading.
• Knowledge of data contracts, raw and curated storage, retention policies, and automated quality checks.
• Strong Python skills with asynchronous HTTP and concurrent processing (asyncio, aiohttp, HTTPX).
• Web scraping experience with Scrapy, Playwright, Selenium, or Beautiful Soup, incl. concurrent scraping and rate limits.

Responsibilities

• Ingest data from multiple sources (APIs, webhooks, files, scraping) with concurrency and error handling.
• Orchestrate batch and event-driven workflows using modern tools (Airflow, Dagster, Prefect, Temporal).
• Implement Pub/Sub or Cloud Tasks-based intake with retries and replay safety.
• Preserve raw payloads in storage (GCS) before transformation with dbt.
• Define medallion layers (raw, standardized, curated) and ensure traceability to sources.
• Ensure data quality via automated validation and pipeline tests; monitor freshness and failures.
• Design and optimize PostgreSQL schemas, queries, and loading patterns for downstream products.
• Integrate provider enrichment and LLM-based structured extraction into the platform.

Skills

SQL

PostgreSQL

Python

Data modeling

Data contracts

Quality checks

Tools

dbt

Airflow

Dagster

Prefect

Temporal

GCP

BigQuery

Dataform

Scrapy

Playwright

Selenium

Beautiful Soup

Job description

Описание

We’re looking for a senior data engineer to own our data platform from architecture through production, reliably turning high volumes of data from provider APIs, webhooks, files, and web scraping into trusted records for our products. Today, our enrichment and extraction workflows largely run serially inside a container. You’ll lead the move to a platform that supports concurrent processing, event-driven intake, and orchestrated batch workflows.

Задачи

• Multi-source intake: Build concurrent ingestion for provider APIs, webhooks, files, and scraping, with rate limits and failure handling.
• Batch and event-driven execution: Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect.
• Build event-driven intake with Pub/Sub or Cloud Tasks, including retries, dead-letter queues, and controlled replay of failed work.
• Raw-to-curated data: Preserve original payloads, including provider JSON, in Google Cloud Storage (GCS) before transforming them with dbt.
• Establish medallion layers for raw, standardized, and curated data.
• Correctness through recovery: Implement idempotent upserts and safe retries, replay, and historical backfills that avoid duplicate records or data corruption.
• Make data traceable to its source and safe to reprocess.
• Data quality and delivery: Define data contracts, build automated validation and pipeline tests, and monitor freshness and failures.
• Design and optimize PostgreSQL schemas, queries, and loading patterns so downstream products receive dependable data.
• Extraction and enrichment: Integrate provider enrichment and LLM-based structured extraction from text into the platform.

Требования

• Experience building multi-source production data platforms from zero to one, from initial architecture and pipeline implementation through deployment and ongoing operation.
• Production experience with workflow orchestration tools such as Airflow, Dagster, Prefect, or Temporal, including dependencies, retries, and failure recovery.
• Airflow experience is preferred.
• Hands-on production experience with dbt and GCP.
• Experience building event-driven systems with messaging or task queues, including asynchronous processing and failure recovery.
• Strong SQL and PostgreSQL skills, including data modeling, query optimization, and reliable incremental loading.
• Experience with data contracts, raw and curated storage, retention policies, and automated quality checks.
• Strong Python skills, including asynchronous HTTP and concurrent processing with tools such as asyncio, aiohttp, or HTTPX.
• Hands-on web scraping experience with tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrent scraping, rate limits, and failure handling.
• Experience working in startups or small engineering teams, with broad ownership and changing priorities.
• Entity resolution experience is especially valuable: matching company and people records across sources and reconciling duplicates into consistent entities.
• LLM-based structured extraction or company and people data enrichment.
• Familiarity with BigQuery and Dataform.