infotreeglobalsolutions

Data Platform Engineer/Lakehouse Architecture

infotreeglobalsolutions

Kraków, PL / Poland, PLPosted Feb 4, 2026
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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, Tiger. Graph) 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, Cloud. Formation, Pulumi, or ARM templates Technical Skills (Preferred):
  • Search : Open. Search, 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 Cloud. Watch 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