Data Platform Engineer/Lakehouse Architecture

Infotree Global Solutions Inc.; Kraków; On-site; PLN 240,000 - 360,000; Full time

Job summary

Infotree Global Solutions Inc. seeks an experienced Data Platform Engineer to design and implement a cloud-based data lakehouse platform that ingests engineering and security tool data, transforms it through multiple layers, and serves analytics dashboards and AI agents.

The role requires a strong track record building lakehouse architectures, production-grade data platforms, and hands-on experience with graph databases and real-time stream processing to support complex data workflows.

Qualifications

• 8+ years in data engineering roles with focus on lakehouse architectures.
• Proven track record delivering production-grade data platforms.
• Experience with graph databases (Neo4j, Neptune, TigerGraph) for relationship modeling.
• Hands-on with stream processing (Kafka, Flink, Spark Streaming, Kinesis).

Skills

Cloud Platforms (AWS)

SQL & Data Modeling

Transformation Tools

Programming (Python/Scala)

Orchestration

IaC

Tools

OpenSearch/Elasticsearch

Neo4j

Spark

Flink

Job description

We are seeking an experienced Data Platform Engineer to design and implement a cloud-based data lakehouse platform that ingests engineering and security tool data, transforms it through multiple layers, and serves it to both analytics dashboards and AI agents.

Experience:

• - 8+ years in data engineering roles, with at least 2 years building lakehouse architectures (Bronze/Silver/Gold or equivalent medallion patterns)
• - Proven track record delivering production-grade data platforms
• - Experience with graph databases (Neo4j, Amazon Neptune, TigerGraph) for relationship modeling
• - Hands-on with stream processing (Kafka, Flink, Spark Streaming, Kinesis)

Technical Skills (Core):

• - Cloud Platforms: Deep expertise in AWS, (S3/Blob, RDS/SQL Database, managed Kafka, serverless compute)
• - SQL & Data Modeling: Expert-level SQL, dimensional modeling, SCD2, normalization vs. denormalization trade-offs
• - Transformation Tools: dbt, Databricks SQL, Dataform, or custom SQL/Python frameworks
• - Programming: Python or Scala for data processing, scripting, and automation
• - Orchestration: Airflow, Prefect, Dagster, Step Functions, or Azure Data Factory
• - IaC: Terraform, CloudFormation, Pulumi, or ARM templates

Technical Skills (Preferred):

• - Search: OpenSearch, Elasticsearch, or Solr for text indexing and retrieval
• - Graph: Neo4j Cypher, SPARQL, or Gremlin for graph queries; experience with graph ETL
• - Data Quality: Great Expectations, dbt tests, or custom validation frameworks
• - Real-time: Flink, Spark Streaming, or serverless event processing (Lambda, Cloud Functions)
• - Monitoring: Grafana, Datadog, or CloudWatch for data pipeline observability

Professional Skills:

• - Communication: Explain technical trade-offs (cost, performance, complexity) to non-technical stakeholders
• - Problem-Solving: Debug data quality issues, optimize slow queries, resolve schema conflicts
• - Collaboration: Work with data scientists, DevOps engineers, and compliance teams
• - Autonomy: Manage ambiguity; propose solutions when requirements are incomplete