Senior Data Engineer

Jobgether; España; Remote; EUR 107,000 - 133,000; Full time

Benefits offered by this job

Fully remote

Technical ownership

Modern tech stack

End-to-end impact

Engineering autonomy

Innovation opportunities

International collaboration

USD salary in monthly terms

Job summary

Jobgether is seeking a Senior Data Engineer based in Spain for full ownership of a modern data platform. You will shape architecture, build scalable ingestion pipelines from APIs, webhooks, files, and web scraping, and evolve workflows to event-driven, concurrent processing on a fully remote team.

Responsibilities include data quality, lineage, and governance, with strong focus on PostgreSQL performance, dbt models, and LLM-based enrichment. English communication is essential.

Qualifications

• Proven experience building production data platforms from the ground up.
• Hands-on experience with Airflow, Dagster, Prefect, or Temporal.
• Experience with Google Cloud Platform (GCP) and dbt.
• Experience with event-driven architectures and concurrency.
• Strong Python skills, asyncio / aiohttp / HTTPX.
• Advanced SQL knowledge and PostgreSQL expertise.
• Data contracts, raw and curated storage, retention policies, data quality monitoring.
• Production scraping pipelines with Scrapy, Playwright, Selenium, or Beautiful Soup.
• Independent ownership and startup/lean-team experience a plus.
• Entity resolution and data enrichment using LLMs.
• Familiarity with BigQuery and Dataform.
• Excellent English communication in a professional environment.

Responsibilities

• Own Data Platform Architecture and operations end-to-end.
• Build multi-source ingestion pipelines for APIs, webhooks, files, and scraping.
• Develop batch and event-driven workflows with orchestration tools.
• Ensure reliable failure recovery and idempotent processing.
• Maintain raw-to-curated data layers in GCS and dbt transformations.
• Define data contracts, validation, tests, and monitoring.
• Optimize PostgreSQL schemas and incremental loading strategies.
• Integrate enrichment and AI-based extraction into the platform.
• Ensure data traceability and safe reprocessing capabilities.
• Drive continuous improvements for throughput and reliability.

Skills

Data engineering

Orchestration

Python

SQL

PostgreSQL

dbt

GCP

Web scraping

Airflow

Dagster

Prefect

Temporal

LLM data processing

BigQuery

Data modeling

English comms

Tools

Airflow

Dagster

Prefect

Temporal

Pub/Sub

Cloud Tasks

dbt

Scrapy

Playwright

Selenium

Beautiful Soup

BigQuery

Dataform

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Senior Data Engineer based in Spain.

As a Senior Data Engineer, you will take ownership of the data platform, shaping its architecture and ensuring reliable performance from development through production. You will build scalable pipelines that transform high volumes of data from APIs, webhooks, files, and web scraping into trusted, product-ready information. A key priority will be evolving existing sequential workflows into a modern platform supporting concurrent processing, event-driven ingestion, and orchestrated batch execution. You will establish robust data quality standards, resilient recovery mechanisms, and transparent data lineage to ensure accuracy and consistency at scale. Working with technologies such as Python, PostgreSQL, dbt, and Google Cloud Platform, you will integrate external data sources and AI-powered extraction capabilities. This fully remote opportunity offers broad technical ownership, the freedom to shape foundational data infrastructure, and the chance to make a direct impact in a collaborative, fast-moving engineering environment.

Accountabilities

• Own Data Platform Architecture: Design, build, and operate a production-grade data platform from initial architecture through deployment, optimization, and ongoing maintenance.

• Build Multi-Source Ingestion Pipelines: Develop concurrent ingestion systems for provider APIs, webhooks, files, and web scraping, incorporating rate limiting, error handling, and reliable processing at scale.

• Develop Batch and Event-Driven Workflows: Implement scheduled pipelines, historical backfills, and dependency management using orchestration tools such as Airflow, Dagster, or Prefect. Build event-driven processing with messaging and task queue services, including Pub/Sub or Cloud Tasks.

• Ensure Reliable Failure Recovery: Implement retries, dead-letter queues, controlled replay mechanisms, and idempotent upserts to recover from failures without generating duplicate records or corrupting data.

• Establish Raw-to-Curated Data Architecture: Preserve original source payloads, including provider JSON, in Google Cloud Storage (GCS), then transform and standardize data using dbt. Maintain clearly defined raw, standardized, and curated data layers.

• Strengthen Data Quality and Governance: Define data contracts, automated validation rules, pipeline tests, retention policies, and monitoring systems to track data freshness, processing failures, and overall reliability.

• Optimize Database Performance: Design and maintain PostgreSQL schemas, queries, and incremental loading strategies to ensure efficient processing and dependable data delivery to downstream products.

• Integrate Enrichment and AI Extraction: Incorporate third-party enrichment providers and large language model (LLM)-based structured extraction from text into the data platform.

• Ensure Data Traceability: Maintain clear lineage between source data and curated records, enabling safe reprocessing, historical recovery, auditing, and consistent data management.

• Drive Continuous Improvement: Identify opportunities to improve throughput, reliability, maintainability, and infrastructure efficiency while adapting the platform to evolving business and product needs.

Requirements

• End-to-End Data Engineering Experience: Proven experience building production data platforms from the ground up, including architecture, pipeline implementation, deployment, monitoring, and ongoing operations.

• Workflow Orchestration: Hands-on production experience with Airflow, Dagster, Prefect, or Temporal, including workflow dependencies, retries, scheduling, and failure recovery. Airflow experience is preferred.

• Google Cloud and dbt: Practical production experience with Google Cloud Platform (GCP) and dbt, including data transformation, pipeline management, and cloud-based data infrastructure.

• Event-Driven Architecture: Experience developing asynchronous, event-driven systems using messaging platforms or task queues, with a strong understanding of concurrent processing, retries, and fault tolerance.

• Python Programming: Strong Python skills, particularly asynchronous HTTP requests and concurrent processing using libraries such as asyncio, aiohttp, or HTTPX.

• SQL and PostgreSQL: Advanced SQL knowledge and practical PostgreSQL experience, including data modeling, query optimization, incremental loading, and performance tuning.

• Data Quality and Modeling: Experience designing data contracts, raw and curated storage layers, retention policies, automated validation, and data quality monitoring.

• Web Scraping Expertise: Hands-on experience building and maintaining production scraping pipelines using tools such as Scrapy, Playwright, Selenium, or Beautiful Soup, including concurrency management, rate limiting, and failure handling.

• Independent Ownership: Ability to make sound technical decisions, manage complex projects, and take responsibility for data infrastructure across the full development lifecycle.

• Startup or Small-Team Experience: Experience working in a startup or lean engineering team, where priorities evolve quickly and engineers take broad ownership, is a plus.

• Entity Resolution: Familiarity with matching company and individual records across multiple sources, identifying duplicates, and consolidating information into consistent entities is highly desirable.

• LLM-Based Data Processing: Experience using large language models for structured data extraction, company enrichment, or people data enrichment is an advantage.

• Additional Data Technologies: Familiarity with BigQuery and Dataform is a plus.

• Communication Skills: Ability to communicate technical concepts clearly, collaborate effectively with distributed teams, and work comfortably in an English-speaking professional environment.

Benefits

• Fully remote work: Work remotely from eligible locations across Europe and Latin America, with flexibility to organize your working environment.

• Technical ownership: Lead the architecture and evolution of a data platform, with meaningful influence over technical decisions and engineering practices.

• Modern technology stack: Work with Python, PostgreSQL, GCP, dbt, event-driven systems, workflow orchestration, web scraping, and AI-powered data extraction.

• End-to-end impact: Build foundational infrastructure that directly supports product functionality, data quality, and business growth.

• Engineering autonomy: Take ownership of technical challenges and shape scalable solutions in a dynamic, product-focused environment.

• Innovation opportunities: Explore advanced data processing, entity resolution, external data enrichment, and LLM-based extraction techniques.

• International collaboration: Work with a distributed team across multiple regions, with opportunities to contribute to evolving technical initiatives.

• Compensation flexibility: Salary expectations are discussed in USD on a monthly basis, with the final package determined through the recruitment process.