Middle Data Engineer

Role SummaryTake the next step in your career by joining a team where you will not just build pipelines, but design scalable data platforms.

We are looking for an experienced Data Engineer who combines strong technical expertise in Python and SQL with a mindset for optimization.

You will work directly with clients to build modern Data Warehouses and Lakehouses using industry-standard tools like Databricks and Azure Data Factory.

The MissionTo build and optimize the data backbone of our clients' businesses.

Your mission goes beyond moving data; you will ensure that pipelines are resilient, scalable, and economically efficient (FinOps).

You will bridge the gap between infrastructure and data, taking ownership of technical delivery and helping shape the architecture of modern data platforms.

The Tech StackCore Languages: Python or Scala (Proficient/OOP), SQL (Advanced/Analytical).Compute & Storage: Databricks, Snowflake, BigQuery, Synapse, Redshift.Orchestration: Azure Data Factory (ADF), Airflow, Dagster.Transformation: dbt (Core/Cloud), Spark/PySpark (Optimization focus).Infrastructure: Terraform (Implementation), Docker, Kubernetes (K8s).

Your ResponsibilitiesData Platform Development: Design and implement pipelines to ingest, clean, and transform data into Data Lakes and Data Warehouses.Performance & FinOps: Actively monitor and optimize pipelines for performance and cost.

Differentiate between a working query and an efficient one.Deployment & Configuration: Configure pipeline deployments and manage environment settings using IaC tools (Terraform) and CI/CD.Quality Assurance: Implement data quality checks (e.g., Great Expectations, dbt tests) and ensure data integrity.Mentorship: Communicate with stakeholders to clarify requirements and provide technical guidance/code reviews to 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 periodA well-equipped and cozy office supports comfort and productivity across all project stages.Welcoming atmosphere and a friendly corporate culture.

Your SkillsExperience: 2+ years of hands-on data engineering experience (ETL/ELT).Data Architecture: Strong understanding of Data Warehousing concepts (Star/Snowflake Schema) and Data Lake principles (Medallion Architecture).Key Platforms: Proven experience with Databricks (managing clusters, notebooks), Azure Data Factory (pipelines, data flows), Snowflake.Engineering Core: Proficiency in Python (OOP, functional programming) and strong SQL skills for complex transformations.Cloud Fluency: Practical experience with at least one major cloud (AWS, GCP, Azure) and its data services (e.g., Kinesis/Lambda, Dataflow/BigQuery, Synapse).Infrastructure Awareness: Base understanding of Infrastructure as Code (Terraform).

Ability to deploy and modify infrastructure resources independently.Communication: Ability to communicate technical decisions directly to clients and the team.

Nice to HaveAdvanced IaC: Experience setting up complex modules in Terraform or Pulumi.Lakehouse Formats: Hands-on experience with Delta Lake or Apache Iceberg features (Time Travel, Schema Evolution).NoSQL: Experience modeling data for NoSQL databases (MongoDB, DynamoDB).Certifications: Associate-level cloud certifications (e.g., Azure Data Engineer Associate, Databricks Certified Data Engineer).