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Senior Data Engineer - Data Ingestion, Enrichment

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Responsibilities

Design, build, and own Preply’s data lake.

Develop and operate scalable, reliable batch and streaming ingestion pipelines.

Define and implement data contracts between producers and consumers.

Build enrichment logic that joins, standardizes, and contextualizes data across domains.

Instrument ingestion pipelines with strong observability: freshness, latency, data quality, and cost metrics.

Apply consistent access control, classification, and privacy protections at ingestion time.

Contribute to standardized ingestion templates, shared libraries, and platform tooling that enable teams to onboard new data sources independently.

Work closely with Product, Backend, Analytics, and ML partners to align on ingestion requirements.

Requirements

Exposure to and experience building architectural patterns of a large, high-scale application (e.g., well-designed APIs, high-volume data pipelines, efficient algorithms).

Solid experience working in platform or data engineering teams (or equivalent impact) with evidence of leading multi-stakeholder deliveries.

Familiarity with cloud platforms (AWS/GCP or equivalent) and modern DevOps practices.

Hands‑on experience designing and implementing real‑time and batch data processing infrastructures using modern frameworks like Spark, Flink, Spark streaming, Kafka, Debezium, etc.

Expertise with orchestration tools such as Airflow, dbt, or similar.

Exceptional problem‑solving skills paired with a proactive, innovative mindset focused on continuous improvement.

Strong communication and cross‑functional collaboration skills (English level B2+)

Core Competencies

Demonstrates expertise in designing and implementing scalable data ingestion pipelines and architectures, with a strong focus on data quality, observability, and cross‑functional collaboration. Proficient in utilizing cloud platforms and orchestration tools to enhance data processing capabilities.

Highest-signal resume keywords

Data Lake Design

Batch And Streaming Ingestion Pipelines

Real-Time Data Processing

Cloud Platforms (AWS/GCP)

Orchestration Tools (Airflow, dbt)

ATS Optimization Keywords

Hard Skills

Data Ingestion

Data Quality Metrics

Data Contracts

Data Standardization

Architectural Patterns

High-Volume Data Pipelines

Efficient Algorithms
Real-Time Processing

Batch Processing

Data Engineering

Soft Skills

Problem-Solving

Proactive Mindset

Innovative Thinking

Communication

Cross-Functional Collaboration

Industry Keywords

Data Engineering

DevOps Practices

Data Privacy
Access Control

Data Classification

Tools & Technologies

Spark

Flink

Kafka

Debezium

Airflow