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We are looking for an experienced Senior Data Engineer to design, build, and optimize modern, cloud-based data platforms that power analytics, AI, and data products across the organization. You will work on scalable batch, streaming, and near-real-time pipelines, enabling high-quality, curated datasets while ensuring robust data governance, security, and observability across the data ecosystem. You will also play a key role in supporting AI and GenAI systems, enabling pipelines for machine learning, causal modeling, and LLM-powered applications such as RAG and agent-based systems. Our preferred platforms are Microsoft Azure / Fabric (primary), GCP, AWS, Databricks, and Snowflake, with Azure experience being highly transferable to Fabric. You will collaborate closely with data scientists, ML engineers, and platform teams to ensure the data foundation supports production-grade, decision-oriented AI systems.
Job Responsibility
Design and implement scalable data platforms and pipelines across cloud environments (Azure/Fabric, AWS, GCP, Databricks, Snowflake)
developing reliable batch, streaming, and near-real-time pipelines using technologies such as Spark and Delta Lake
building ingestion, transformation, and curation workflows for both structured and unstructured data
implement modern data architectures including lakehouse patterns and medallion layering (bronze, silver, gold)
deliver high-quality datasets that support analytics, machine learning, causal modeling, and optimization systems
enable data pipelines for GenAI use cases (including LLMs, RAG pipelines, and vector-based data flows)
design scalable logical and physical data models for analytical and operational use cases
orchestrate workflows using tools such as Airflow, dbt, Lakeflow, or equivalents
apply modern architecture patterns including event-driven and streaming architectures
ensure adherence to best practices in data governance, lineage, quality, and access control (RBAC/ABAC)
establish strong data observability
enable data serving layers (APIs, feature inputs, analytical endpoints)
monitor and optimize pipelines and infrastructure for performance, scalability, and cost efficiency
work closely with data scientists, ML engineers, analysts, and business stakeholders to translate requirements into robust data solutions
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, and governance principles
Experience with one or more major cloud platforms, with preference for Microsoft Azure / Fabric, as well as AWS or GCP
Familiarity with modern data platforms such as Databricks and Snowflake
Experience with lakehouse architectures and distributed data systems
strong understanding of scalability, reliability, and performance considerations in data pipelines
Strong problem-solving skills focused on scalability and reliability
collaborative approach to working in cross-functional teams
Nice to have
Experience with GenAI and AI data systems (e.g., RAG pipelines, vector databases, LLM data preparation)
CI/CD for data pipelines and infrastructure-as-code tools such as Terraform, ARM, or CloudFormation
Additional exposure to streaming technologies (e.g., Kafka)
Spark optimization
advanced analytics and ML workloads (including causal or experimentation platforms)
Experience building data products or large-scale analytics platforms
What we offer
Health insurance that guarantees fast access to contracted health services
Vacation Plan
Subsidy for study materials, trainings, conferences and events that will contribute to your development
Hybrid Working model
Performance Evaluation Process that paves the roadmap for a personal and professional career development
Refreshments and fruit in the office
Team gatherings and parties organized and subsidized by the company