Medior Data Engineer

Role Summary You own pipelines end to end: ingestion to modeled warehouse, deployed through CI/CD, monitored, and cost-aware. Projects vary - a lakehouse on one engagement, a classic warehouse on the next - so you work from principles, not from one platform's playbook. Data engineering is your profession, not a side skill picked up on a backend project - and you explain your decisions to engineers and stakeholders alike.

The Mission Build reliable, transparent, cost-effective data pipelines and help clients understand the value behind our technical choices.

The Tech Stack Core Languages: Python (OOP, typing, testing), SQL (advanced). Scala welcome.

Platforms: Databricks, Snowflake, BigQuery, Synapse, Redshift.

Orchestration: Airflow or Azure Data Factory; Dagster.

Transformation: dbt (Core/Cloud), Spark/PySpark (optimization focus).

Streaming: Kafka, Kinesis, or Event Hubs (working basics).

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

Your Responsibilities Design and build pipelines: ingest, clean, transform into lakes and warehouses.

Optimize for performance and cost - know a working query from an efficient one (FinOps).

Deploy through CI/CD and Terraform; keep environments reproducible.

Implement quality checks and freshness monitoring; alert before the client notices.

Clarify requirements with stakeholders; review and guide 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.

Your Skills Experience: 2+ years of hands-on data engineering (ETL/ELT in production).

Data Architecture: star/snowflake schemas, SCD, Medallion architecture, and the working vocabulary of the craft: idempotency, backfills, late-arriving data, batch vs streaming trade-offs.

Key Platforms: proven experience with Databricks (clusters, jobs, Delta) or Snowflake; orchestration with Airflow or ADF.

Engineering Core: Python (OOP, tests) and SQL for complex transformations - including reading query plans.

Cloud Fluency: one major cloud and its data services (Kinesis/Lambda, Dataflow/BigQuery, Synapse).

Infrastructure: Terraform - deploy and modify resources independently.

Data Quality: dbt tests or Great Expectations in practice; you monitor what you ship.

Communication: explain technical decisions to clients and the team.

Nice to Have Advanced IaC: complex Terraform or Pulumi modules.

Lakehouse Formats: Delta Lake or Iceberg internals - time travel, schema evolution.

Streaming: a production streaming pipeline you built or maintained.

NoSQL: data modeling for MongoDB or DynamoDB.

Certifications: associate level (Azure Data Engineer, Databricks Certified DE).