Senior Data Engineer

Role Summary

You are the technical authority on client projects. You design solutions from the ground up, review and improve existing systems, define architectures, estimate delivery efforts, and guide teams through successful implementation.

Client technologies will vary, but strong technical judgment remains constant. You know when the simplest solution is the right one. You understand the discipline of data engineering, not just the tools used to implement it.

The Mission

Build platforms that scale with the business, pass audits, and stay reliable, even at 3 a.m. Enable teams to become stronger after every engagement.

The Tech Stack

• Core Languages: Python (patterns, library design), SQL (internals).

• Platforms: Databricks (Unity Catalog), Snowflake, BigQuery, Synapse.

• Processing & Streaming: Spark (internals, tuning), Kafka / Structured Streaming, dbt (enterprise patterns).

• Architecture: Lakehouse (Delta/Iceberg), Data Mesh, Lambda/Kappa.

• Governance & Quality: catalogs, lineage, RBAC/masking, data contracts, observability.

• Infrastructure & DevOps: advanced Terraform, CI/CD, Docker/Kubernetes.

• Emerging: feature stores, vector databases, LLM/RAG data pipelines.

Your Responsibilities

• Own architecture on engagements; act as the Design Authority.

• Solve the hardest problems: streaming under load, Spark debugging, framework design.

• Drive presales: assessments, audits, estimations; explain the ROI of modernization.

• Make solutions production-ready: secure, observable, cost-efficient, documented - with quality SLAs defined.

• Set standards, review code, grow Middle and Junior engineers.

What We Offer

• Long-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 period

• A well-equipped and cozy office supports comfort and productivity across all project stages.

• Welcoming atmosphere and a friendly corporate culture.

Requirements

Your Skills

• Experience: 5+ years of data engineering, with delivery you owned end to end.

• Engineering Mastery: expert Python and SQL; you optimize code others wrote.

• Craft Fluency: the discipline's concepts explained clearly enough to teach: modeling methodologies (Kimball, Data Vault), processing paradigms, consistency and delivery guarantees.

• Platform Depth: Databricks (Spark memory, partitioning) or Snowflake (warehouse tuning, RBAC, zero-copy cloning) - how they work under the hood.

• Streaming: production experience with Kafka or Kinesis plus Spark Structured Streaming or Flink.

• Governance: design access models, lineage, and quality SLAs a client can pass an audit with.

• Architectural Vision: end-to-end design, tool selection with trade-offs ("Why Snowflake over Redshift?"), decisions defended to leadership.

• Cloud Mastery: one major cloud at expert level - networking (VPC, PrivateLink), security (IAM), service limits.

• Consulting: presales, technical audits, discovery; business needs into technical specs.

• AI/ML Readiness: feature engineering, data pipelines for LLM/RAG.

Nice to Have

• GenAI Stack: vector databases (Pinecone, pgvector, Weaviate), LangChain-style frameworks.

• Streaming Depth: Flink or Kafka Streams in production.

• Certifications: professional level (AWS Solutions Architect Pro, Databricks DE Professional).

• NoSQL: advanced modeling for DynamoDB, Cosmos DB, or MongoDB.