BENCH+ PROFILE

Gleb

Data Engineer
Data and ML platform engineering for regulated risk, credit, antifraud, and e-commerce analytics systems

Lead-level Data Engineer with hands-on delivery across batch and streaming pipelines, data warehouse architecture, feature and score storage, production monitoring, and ML-adjacent platform components in regulated and high-volume commercial environments.

BEST-FIT USE CASE

Best suited for a data platform or risk-data engineering role that needs a hands-on engineer to design ingestion, marts, monitoring, and ML scoring data flows while coordinating delivery across data, ML, backend, and analytics teams.

Not the primary fit: Less aligned with roles focused primarily on frontend development, pure model research, standalone product management, or security operations.

EVIDENCE HIGHLIGHTS

What supports the assessment

Owned and implemented ML-data platform components for a regulated credit and antifraud environment, including batch inference, online scoring data flows, feature storage, score storage, credit marts, and streaming ingestion.

Designed a multi-model scoring data layer using Spark, container orchestration, object storage, and Kafka-backed delivery into backend systems.

Built a Go-based online scoring microservice on container orchestration for real-time credit decisioning workflows.

Implemented a custom ingestion framework connecting many upstream streaming and batch inputs into analytics and decisioning pipelines.

Created production checks for feature distributions, counts, freshness, score outputs, reporting, and alerting.

In a banking risk environment, optimized offline inference and heavy analytical marts on Hadoop and Spark, then deployed risk models into production data workflows.

Designed a PostgreSQL data warehouse from scratch for an e-commerce analytics environment, including staging, marts, indexing, partitioning, scheduling, and migration of spreadsheet-based reporting.

Delivered hands-on training on data architecture and MLOps concepts, guiding participants through a cloud-hosted model inference service.

WORKING STYLE

How they show up

Explains technical work in a structured sequence with business context, implementation detail, and delivery outcome.
Comfortable operating as the first data engineer, as a hands-on technical lead, and as part of a larger cross-functional risk or analytics group.
Uses decomposition, metrics, alerts, and quality gates when diagnosing implementation problems.
Considers total cost of ownership, team skills, lifecycle, scale, compliance, infrastructure constraints, and maintainability when choosing a stack.
Has experience coordinating access, approvals, and delivery across multiple technical and business stakeholders.

KEY STRENGTHS

Designs and implements batch and streaming ETL and ELT pipelines for regulated data workflows.
Builds data warehouse models, marts, feature storage, score storage, and ingestion frameworks from upstream systems.
Implements data-quality, score-quality, freshness, and model-output monitoring with alerts and dashboards.
Optimizes Spark and SQL workloads through partitioning, caching, query rewrites, and data-layout changes.
Has led data architecture, delivery decomposition, engineering standards, hiring, onboarding, and cross-team coordination while remaining hands-on.
Connects data engineering work with ML lifecycle needs, including offline inference, online scoring data flows, training datasets, and validation workflows.

PRIMARY SKILLS

PythonSQLSparkKafkaAirflowKubernetesDockerHadoopPostgreSQLGreenplum

ADDITIONAL TECHNOLOGIES

GoHiveS3MLflowCatBoostFastAPIGitLab CIQlik SenseOpenCV

INDUSTRY EXPERIENCE

AI / DataE-commerceFintechMediaEnterprise SoftwareRetail

ROLE MATCH

Data EngineerStrong

Data / ML EngineerPartial

MLOps EngineerPartial

Backend EngineerPartial