Data Engineer

7y relevant experience

Qualified

For hiring agencies & HR teams

EU engineers, ready to place with your US clients

Pre-screened on AI. Remote B2B contracts. View 5 full profiles free — AI score, skills report, interview questions included.

Executive Summary

This candidate is a technically strong senior-level data and ML engineer with 9 years of experience and direct hands-on exposure to the majority of required stack components including Python, Spark, Airflow, Kafka, and multi-cloud infrastructure. The candidate's deep ML/LLM background is a significant differentiator for an AI-first recruiting platform and suggests they can bridge data engineering and ML engineering workflows effectively. The primary technical gap is dbt, which is learnable and should not be disqualifying given the overall profile strength. Key pre-offer actions include clarifying the 17-month employment gap, conducting a technical coding assessment to validate hands-on capabilities, and assessing alignment with the startup's bias-toward-shipping culture. The candidate is likely to expect compensation above the posted range given their seniority level.

Top Strengths

✓9 years of progressive experience spanning data engineering, ML engineering, and DevOps — directly aligned with the role's cross-functional expectations
✓Hands-on Kafka and real-time streaming experience satisfies a key preferred qualification
✓Strong Airflow and Apache Spark expertise matches the core technical stack requirements precisely
✓ML/LLM domain knowledge enables unusually effective collaboration with the ML engineering team on dataset preparation and feature pipelines
✓Multi-cloud experience (AWS, GCP, Azure) with infrastructure-as-code (CDK, Kubernetes) demonstrates production-level deployment maturity

Key Concerns

!Unexplained 17-month employment gap (August 2021 – November 2022) requires clarification during screening
!Absence of LinkedIn, GitHub, and any verifiable professional presence reduces candidate verifiability and raises mild credibility questions at the senior level

Culture Fit

75%

Growth Potential

High

Salary Estimate

$85k–$100k (likely above posted range given 9 years experience and senior title; negotiate carefully)

Assessment Reasoning

This candidate is based on Lei Wang meeting approximately 85% of required technical skills with direct experience in Python, SQL, Apache Spark, Airflow, ETL/ELT workflows, AWS/GCP, and Kafka. The candidate's 9-year trajectory demonstrates progressive seniority and production-level ownership across multiple data-intensive environments. The ML/LLM specialization is a genuine value-add for this AI-first platform beyond the standard data engineering scope. The missing dbt skill and unclear Snowflake/BigQuery direct experience are addressable gaps that do not materially undermine fit for a mid-level role definition. The employment gap and lack of verifiable online presence warrant investigation but are not sufficient to downgrade to BORDERLINE without further evidence. This candidate is moderated to 78 due to the inability to verify code quality, employment history through LinkedIn, or assess professional reputation through community presence.

Interview Focus Areas

Deep-dive on data pipeline architecture decisions: ask candidate to walk through a complex ETL/ELT pipeline they designed end-to-end, including failure handling and monitoringSQL optimization and data warehouse design: probe Snowflake/BigQuery familiarity and query performance tuning given the absence of explicit experiencedbt familiarity or willingness to adopt: assess learning agility and how quickly candidate can ramp on dbt transformation workflowsEmployment gap clarification: transparent discussion of the August 2021 – November 2022 periodStartup/autonomy fit: explore how candidate has handled ambiguity, prioritization, and ownership in previous roles given the flat remote-first culture

Code Review

FairSenior Level

Without a GitHub profile or code samples, direct code quality assessment is not possible and the score reflects this limitation rather than inferred incompetence. Based on the breadth and seniority of roles described, the candidate likely writes production-grade code, but this cannot be confirmed without a technical screen or take-home assessment. A coding challenge focusing on Spark pipeline design and SQL optimization is strongly recommended.

PythonPySparkScalaFastAPIFlaskDjangoPytestDockerKubernetes

+Demonstrated use of TDD principles and Pytest in Python development roles
+Experience with CI/CD pipelines (AWS CodePipeline, GitHub Actions) suggests awareness of code quality gates
+Broad multi-language proficiency (Python, Scala, Java, Go) indicates architectural versatility

-No GitHub profile provided — cannot directly assess code quality, style, or open-source contributions
-Resume descriptions are achievement-oriented but lack specifics on code architecture decisions or design patterns applied
-No evidence of contributions to open-source data projects, which was a preferred qualification

Experience Overview

9y total · 7y relevant

Lei Wang presents a strong 9-year profile with deep overlap across required data engineering tools including Python, Spark, Airflow, Kafka, and cloud platforms. The candidate brings valuable ML/LLM context that directly supports the AI-first recruiting platform's needs. The primary gap is dbt and explicitly demonstrated Snowflake/BigQuery experience, though adjacent skills suggest a manageable learning curve.

Matching Skills

PythonSQLApache SparkAirflowETL/ELTAWS/GCPKafkaData PipelinesDockerKubernetesMLflowDatabricksPostgreSQL

Skills to Verify

dbtSnowflake/BigQuery (direct experience unclear)Data quality frameworks (Great Expectations, etc.)

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