Senior Data Engineer

Role SummaryLead the data strategy and engineering excellence for our global clients.

We are looking for a Senior Data Engineer who acts as a trusted technical advisor - someone who can design complex Lakehouse architectures, defend technical decisions before stakeholders, and drive presales activities.

You will combine deep technical expertise in Python, SQL, and distributed computing with high-level architecture, ensuring our solutions are scalable, secure, and future-proof.

The MissionTo be the architect of value.

Your mission is to design scalable, secure, and cost-effective data platforms that solve critical business problems.

You will lead technical audits, define best practices for the team, and build the foundational infrastructure that enables advanced analytics and AI/GenAI capabilities for our clients.

The Tech StackCore Languages: Python or Scala (Expert/Patterns), SQL (Expert/Internals).Compute & Storage: Databricks (Unity Catalog), Snowflake, BigQuery, Synapse.Processing: Spark/PySpark (Deep internals, Tuning, Streaming), dbt (Enterprise patterns).Architecture Patterns: Data Mesh, Lakehouse (Delta Lake/Iceberg), Lambda/Kappa.Infrastructure & DevOps: Advanced Terraform/IaC, CI/CD, Docker/Kubernetes.Emerging Tech: Feature Stores, Vector Databases, MLOps basics.

Your ResponsibilitiesArchitecture & Leadership: Lead the design and implementation of scalable data pipelines and Lakehouse architectures.

Act as the Design Authority.Advanced Engineering: Solve the hardest technical challenges—optimizing high-load streaming pipelines, debugging complex Spark jobs, and designing generic frameworks.Consulting & Presales: Participate in technical assessments, audits of existing systems, and proposal estimations.

Explain the ROI of technical modernization.Performance & Security: Ensure all solutions are production-ready: secure, monitored, cost-efficient (FinOps), and documented.Mentorship: Define coding standards, conduct code reviews, and mentor Middle/Junior engineers to foster a culture of engineering excellence.

What We OfferLong-term career stability with a competitive salary paid in USD.Conditions for steady career development.Development supported by dedicated mentors and a variety of programs focused on expertise and innovation.Private medical insurance provided after successful completion of the probationary periodA well-equipped and cozy office supports comfort and productivity across all project stages.Welcoming atmosphere and a friendly corporate culture.

Your SkillsEngineering Mastery: Expert-level proficiency in Python (design patterns, library development) and SQL.

Ability to optimize code others wrote.Deep Platform Expertise: Mastery of Databricks (Spark memory management, partitioning strategies) or Snowflake (Warehouse tuning, RBAC, Zero-Copy Cloning).

You understand how they work under the hood.Architectural Vision: Ability to design end-to-end data solutions, select the right tools (e.g., “Why Snowflake over Redshift?”), and defend decisions to client leadership.Cloud Mastery: Expert-level knowledge of AWS, GCP, or Azure.

Deep understanding of networking (VPC, PrivateLink), security (IAM), and integration limits.Consulting & Business: Experience participating in Presales, technical audits, or discovery phases.

Translating business needs into technical specs.AI/ML Readiness: Understanding of engineering data for Machine Learning (Feature Engineering, pipelines for LLM/RAG).

Nice to HaveStreaming: Deep experience with Kafka, Kinesis, or Spark Structured Streaming.GenAI Stack: Experience with Vector Databases (Pinecone, pgvector, Weaviate) or frameworks like LangChain.Certifications: Professional-level cloud certifications (e.g., AWS Solutions Architect Pro, Databricks Certified DE Professional).NoSQL: Advanced modeling for DynamoDB, Cosmos DB, or MongoDB.

Your SkillsEngineering Mastery: Expert-level proficiency in Python (design patterns, library development) and SQL.

Ability to optimize code others wrote.Deep Platform Expertise: Mastery of Databricks (Spark memory management, partitioning strategies) or Snowflake (Warehouse tuning, RBAC, Zero-Copy Cloning).

You understand how they work under the hood.Architectural Vision: Ability to design end-to-end data solutions, select the right tools (e.g., “Why Snowflake over Redshift?”), and defend decisions to client leadership.Cloud Mastery: Expert-level knowledge of AWS, GCP, or Azure.

Deep understanding of networking (VPC, PrivateLink), security (IAM), and integration limits.Consulting & Business: Experience participating in Presales, technical audits, or discovery phases.

Translating business needs into technical specs.AI/ML Readiness: Understanding of engineering data for Machine Learning (Feature Engineering, pipelines for LLM/RAG).

Nice to HaveStreaming: Deep experience with Kafka, Kinesis, or Spark Structured Streaming.GenAI Stack: Experience with Vector Databases (Pinecone, pgvector, Weaviate) or frameworks like LangChain.Certifications: Professional-level cloud certifications (e.g., AWS Solutions Architect Pro, Databricks Certified DE Professional).NoSQL: Advanced modeling for DynamoDB, Cosmos DB, or MongoDB.