Role Summary

You lead the technical side of client engagements: design the architecture, plan the work, and lead the team that delivers it. You are accountable for what ships - architecture, quality, and timelines - and the role stays hands-on: writing code, reviewing it, and troubleshooting production are part of the job. Platforms differ from client to client, so we look for principles and depth rather than experience with one specific vendor stack.

The Mission

Data platforms that work in production: correct data, predictable cost, and a client team able to run them after we leave.

The Tech Stack

• Core languages: Python, SQL.

• Processing: Spark/PySpark, including tuning and troubleshooting.

• Orchestration & transformation: Airflow or Dagster; dbt.

• Platforms: Databricks, Snowflake, BigQuery, or Synapse - depth in at least one, and the basis to choose between them.

• Streaming: Kafka, Kinesis, or Event Hubs; Spark Structured Streaming or Flink.

• Architecture: lakehouse formats (Delta, Iceberg), dimensional modeling, Lambda/Kappa.

• Governance & quality: catalogs, lineage, access control, data contracts, monitoring.

• Infrastructure: Terraform, CI/CD, Docker; Kubernetes basics.

• Cloud: AWS, Azure, or GCP - one at an advanced level.

Your Responsibilities

• Own technical delivery on engagements: architecture, plan, quality, and timelines.

• Lead the engineering team day-to-day - distribute work, review code, unblock people.

• Build alongside the team: you write and review code, not only design it.

• Set technical standards on the project - monitoring, quality checks, CI/CD, reproducible environments.

• Support presales: assessments, technical audits, estimates.

• Mentor Middle and Senior engineers, and take part in technical interviews.

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: 6+ years in data engineering, including at least one platform you designed and delivered end-to-end.

• Team leadership: lead teams of 3-8 engineers - plan and distribute work, review code, unblock people, and stay accountable for what the team ships.

• Delivery ownership: estimate, plan, and re-plan; flag risks early; keep scope and timelines realistic.

• Python: production code - modules, tests, packaging; frameworks other engineers build on.

• SQL: complex transformations, execution plans, optimization on large tables.

• Spark: partitioning, shuffles, memory, skew - you debug jobs from the Spark UI and logs.

• Modeling: dimensional models, SCD, incremental loads, backfills, late-arriving data.

• Streaming: at least one production pipeline - delivery guarantees, watermarks, state.

• Governance: access models, lineage, quality checks, and the SLAs around them.

• Infrastructure: environments provisioned as code and deployed through CI/CD.

• Cost: you can explain what a workload costs and reduce it.

• Client work: requirements gathering, estimates, technical explanations to non-engineers.

• Growing engineers: mentoring, code review, and setting technical standards on the project.

• English: B2 or higher. 

Nice to Have

• Migrations: on-premises to cloud, legacy warehouse to lakehouse, or cross-cloud.

• Regulated domains: finance, healthcare, or telecom - GDPR, HIPAA, or SOC 2.

• Certifications: professional level (Databricks, AWS, Azure, GCP).

• Scala: for Spark workloads.

• Kubernetes: in production.