Senior Data Engineer

Clera
Worldwide Remote
Remote

Who this role is best for

Aimed at experienced data engineers who have built and operated multi-source data platforms.

Best fit for

  • Candidates with 5+ years of end-to-end data platform ownership and strong orchestration tool experience
    — “5 to 10 or more years of experience, including production ownership of multi-source data platforms from architecture through deployment and ongoing operation.”
  • Individuals with a background in event-driven systems and asynchronous processing
    — “Experience building event-driven systems with messaging or task queues and asynchronous processing.”
  • Professionals adept at integrating LLM-based structured extraction and data enrichment workflows
    — “Integrate data enrichment and LLM-based structured extraction into the platform.”

Things to consider

  • No visa sponsorship available, which may limit eligibility for non-US candidates
    — “Visa sponsorship is not available.”
  • The role requires adaptability and comfort in small teams with broad responsibilities
    — “Comfort working in a startup or small team with broad ownership and adaptability.”

How to stand out

  • Highlight experience with Airflow, dbt, and PostgreSQL in your resume and interview examples
    — “Strong hands-on experience with Airflow or similar orchestration tools, dbt, and Google Cloud Platform.”
  • Showcase your ability to design robust data ingestion pipelines with failure handling
    — “Ingest data from provider APIs, webhooks, files, and web scraping, with rate limiting and robust failure handling.”
  • Demonstrate your skills in building messaging and task queue workflows with retries and dead-letter queues
    — “Build messaging and task queue workflows with retries, dead-letter queues, and controlled replay.”
  • Emphasize your background in data modeling and query optimization for PostgreSQL
    — “Strong SQL and PostgreSQL skills, including data modeling, query optimization, and incremental loading.”
  • Include examples of automated data quality checks and pipeline monitoring in your application materials
    — “Develop idempotent upserts, data contracts, automated quality checks, pipeline tests, and monitoring for freshness and failures.”
Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • designed and built a multi-source data platform
  • ingested data from various sources
  • orchestrated workflows
  • developed idempotent upserts
Typical background
5+ years of experience in data engineeringstrong hands-on experience with Airflow and dbt

Skills & requirements

Required

Multi-source Data PlatformAirflowdbtGoogle Cloud PlatformEvent-driven SystemsSQLPostgreSQLPythonWeb ScrapingData Quality Checks

Preferred

Llm-based Structured ExtractionBigQueryDataform

Stack & domain

Multi-source Data PlatformAirflowDagsterPrefectGoogle Cloud PlatformdbtPostgreSQLData ModellingQuery OptimizationIncremental LoadingPythonConcurrent Or Asynchronous ProcessingWeb ScrapingData ContractsStorage LayersAutomated Data Quality ChecksEntity ResolutionBigQueryDataformLlm-based Structured ExtractionProblem SolvingAttention To DetailCommunicationCollaborationWorking In A Startup Or Small TeamAdaptabilityData Engineering

About the role

Original posting from Clera via Ashby

ABOUT THE ROLE

Join a data infrastructure and analytics team building a platform that turns information from many sources into reliable, current company intelligence. You will own the platform end to end, from architecture and ingestion through production operations, helping power downstream products with trusted data.

WHAT YOU'LL DO

  • Design and build a multi-source data platform for concurrent ingestion, event-driven processing, and orchestrated batch workflows.
  • Ingest data from provider APIs, webhooks, files, and web scraping, with rate limiting and robust failure handling.
  • Orchestrate scheduled workflows and historical backfills using tools such as Airflow, Dagster, or Prefect.
  • Build messaging and task queue workflows with retries, dead-letter queues, and controlled replay.
  • Preserve original payloads in Google Cloud Storage and transform data with dbt into raw, standardized, and curated layers.
  • Develop idempotent upserts, data contracts, automated quality checks, pipeline tests, and monitoring for freshness and failures.
  • Design and optimize PostgreSQL schemas and loading patterns for reliable downstream use.
  • Integrate data enrichment and LLM-based structured extraction into the platform.

WHAT WE'RE LOOKING FOR

  • 5 to 10 or more years of experience, including production ownership of multi-source data platforms from architecture through deployment and ongoing operation.
  • Strong hands-on experience with Airflow or similar orchestration tools, dbt, and Google Cloud Platform.
  • Experience building event-driven systems with messaging or task queues and asynchronous processing.
  • Strong SQL and PostgreSQL skills, including data modeling, query optimization, and incremental loading.
  • Proficiency in Python and concurrent or asynchronous processing, such as asyncio, aiohttp, or HTTPX.
  • Practical web scraping experience with tools such as Scrapy, Playwright, or Selenium.
  • Experience with data contracts, storage layers, and automated data quality checks. Entity resolution is valuable; BigQuery, Dataform, and LLM-based structured extraction are helpful.
  • Comfort working in a startup or small team with broad ownership and adaptability.

COMPENSATION & BENEFITS

Visa sponsorship is not available.

LOCATION

Remote.

Source: Clera careers (Ashby)

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