Инженер данных

EPAM provides digital platform engineering and software development services, delivering enterprise software products, open source solutions, and accelerators.

• Design scalable, maintainable dbt project structures using modular SQL modeling across staging, intermediate and mart layers;
• Develop reusable dbt components including Jinja macros and custom materializations;
• Implement dbt schema and custom tests to ensure data integrity;
• Write highly performant SQL for analytical workloads with CTEs, window functions and query optimization;
• Integrate dbt with CI/CD pipelines using Git branching strategies and pull request workflows;
• Orchestrate dbt with tools such as Airflow, Dagster and Prefect;
• Apply dimensional modeling and data vault techniques and design semantic layers for BI tools;
• Monitor pipelines, SLAs and lineage while performing root cause analysis on data issues;
• Conduct code reviews, write unit tests for macros and SQL logic and establish design patterns for analytical engineering;
• Optimize warehouse performance through query profiling, incremental models and change data capture;
• Mentor junior engineers, set coding standards and lead architecture discussions;
• Plan engineering roadmaps, manage data debt and ensure the reliability of production pipelines.

• 5+ Years of experience in data engineering with expertise in advanced dbt modeling and testing;
• Proficiency in modular SQL modeling, Jinja macros and custom materializations;
• Mastery of SQL including CTEs, window functions and query optimization;
• Skills in Git, branching strategies and CI/CD tools such as GitHub Actions, Azure DevOps and GitLab CI;
• Expertise in data warehouse platforms such as Snowflake, BigQuery and Databricks or Redshift;
• Knowledge of warehouse performance tuning, partitioning and clustering and cost optimization;
• Understanding of modern ELT patterns and orchestration with dbt Cloud jobs;
• Background in data modeling including Kimball dimensional modeling and data vault;
• Competency in data quality and observability using Great Expectations and Soda;
• Familiarity with cloud platforms such as Azure, AWS and GCP;
• Skills in Infrastructure as Code using Terraform, CloudFormation and dbt Cloud environment automation;
• Capability to translate business requirements into data models and present technical decisions to non-technical stakeholders;
• Nice to have: Proficiency in Python for ingestion or orchestration, Skills in Spark for large data volumes, Familiarity with metadata and catalogs such as Alation, DataHub and Collibra, Knowledge of metrics layers such as dbt Semantic Layer, LookML and Cube.

• Remote work.