Data Engineer

Point Wild (Formerly Pango Group)

full-time

Posted on: 9/5/2025

Location: 🇵🇱 Poland

✨ AI Apply

Mid-LevelSenior

AirflowAWSAzureCloudCyber SecurityGoogle Cloud PlatformKafkaPySparkPythonSparkSQLUnity

About the role

Build and optimize data ingestion pipelines on Databricks (batch and streaming) to process structured, semi-structured, and unstructured data.
Implement scalable data models and transformations leveraging Delta Lake and open data formats (Parquet, Delta).
Design and manage workflows with Databricks Workflows, Airflow, or equivalent orchestration tools.
Implement automated testing, lineage, and monitoring frameworks using tools like Great Expectations and Unity Catalog.
Build integrations with enterprise and third-party systems via cloud APIs, Kafka/Kinesis, and connectors into Databricks.
Partner with AI/ML teams to provision feature stores, integrate vector databases (Pinecone, Milvus, Weaviate), and support RAG-style architectures.
Optimize Spark and SQL workloads for speed and cost efficiency across multi-cloud environments (AWS, Azure, GCP).
Apply secure-by-design data engineering practices aligned with Point Wild’s cybersecurity standards and evolving post-quantum cryptographic frameworks.

At least 5 years in Data Engineering with strong experience building production data systems on Databricks.
Expertise in PySpark, SQL, and Python.
Strong expertise with various AWS services.
Strong knowledge of Delta Lake, Parquet, and lakehouse architectures.
Experience with streaming frameworks (Structured Streaming, Kafka, Kinesis, or Pub/Sub).
Familiarity with DBT for transformation and analytics workflows.
Strong understanding of data governance and security controls (Unity Catalog, IAM).
Exposure to AI/ML data workflows (feature stores, embeddings, vector databases).
Detail-oriented, collaborative, and comfortable working in a fast-paced innovation-driven environment.
Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (bonus).