Senior Data Engineer

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 Germany. 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.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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