1 month ago
Remote, PolandSenior
Responsibilities
- Design and implement scalable cloud data platforms and batch, streaming, and near-real-time pipelines.
- Build ingestion, transformation, curation, lakehouse, and medallion-layer workflows for structured and unstructured data.
- Deliver production-grade datasets and pipelines supporting analytics, machine learning, causal modeling, optimization, GenAI, RAG, vector-based data flows, and agent-based systems.
- Create logical and physical data models and orchestrate workflows using data engineering and scheduling tools.
- Apply data governance, lineage, quality, access-control, and observability practices, including monitoring freshness, reliability, and SLA adherence.
- Build data-serving layers and integrations for APIs, feature inputs, analytical endpoints, ML platforms, and AI systems.
- Optimize pipelines and infrastructure for performance, scalability, reliability, and cost efficiency.
- Discover and refine business requirements by understanding stakeholder goals, identifying gaps, and recommending better alternatives.
- Collaborate with data scientists, ML engineers, analysts, platform teams, and business stakeholders.
- Contribute to data and AI engineering best practices and support adoption of data products.
Requirements
- Strong hands-on experience with Apache Spark and Delta Lake.
- Strong programming skills in Python and SQL.
- Proven experience building batch and streaming data pipelines and production-grade data platforms.
- Solid understanding of data modeling, data quality, governance, lakehouse architectures, distributed data systems, scalability, reliability, and performance.
- Strong hands-on experience with either AWS data services and Databricks or Microsoft Azure/Fabric and its data tooling.
- AWS-track experience may include S3, Glue, EMR, Redshift, Kinesis, Lambda, and the Databricks Lakehouse Platform.
- Azure/Fabric-track experience may include Data Factory, Synapse, Fabric Lakehouse, and Azure Databricks.
- Curiosity, strong problem-solving skills, collaborative working style, and ability to derive and challenge business requirements.
- Experience in Agile or consulting environments is beneficial.
- Experience with both AWS and Azure/Fabric, Databricks and native Fabric tooling, is a strong plus.
- Experience with GenAI data systems, RAG pipelines, vector databases, LLM data preparation, CI/CD for data pipelines, Terraform, or CloudFormation is beneficial.
- Additional experience with Kafka, Spark optimization, advanced analytics and ML workloads, causal or experimentation platforms, data products, or large-scale analytics platforms is valuable.
Benefits
- Position based in Poland.
- 24 working days of paid vacation and national holidays covered.
- Sick leave of up to 20 days per year and unpaid leave of up to 20 days per year.
- Medical insurance, Multisport card or Multikafeteria, and maternity and paternity leave support.
- Internal workshops, learning initiatives, professional certification reimbursement, and professional communities.
- Growth Framework, mentoring opportunities, career advancement, international mobility, and professional development programs.
- Remote and hybrid work options, country-dependent.
- Access to training, cutting-edge tools, and industry experts.
- Progressive benefit packages with additional benefits tied to length of employment.
Tech Stack
Amazon RedshiftApache AirflowApache KafkaApache SparkAWSAzureDatabricksdbtGoogle Cloud PlatformMLflowPythonSnowflakeSQLTerraform
Categories
Data Engineering
About Valtech
Valtech provides digital consulting and engineering services to large enterprises, delivering customer experience design, commerce platforms, data/AI solutions, and transformation programs. It works across sectors such as retail, financial services, manufacturing, mobility, health, and travel, offering strategy, design, build, and managed services. Founded in 1993, Valtech is a global company privately held under Kharis Capital.