Lead GCP Data Engineer

HMG AMERICA LLC; United States; On-site; USD 180,000 - 260,000; Full time

Job summary

HMG AMERICA LLC is seeking an experienced Lead GCP Data Engineer to design, architect, and scale our enterprise data infrastructure on Google Cloud Platform. You will lead the technical vision for our data environment and drive end-to-end ELT/ETL pipeline architecture.

You will mentor a team of engineers and collaborate with data scientists, software engineers, and business stakeholders to turn complex data into actionable BI and AI-ready data products.

Qualifications

• 8+ years of total data engineering experience.
• 4+ years of hands-on leadership in GCP data implementations at enterprise scale.
• Strong proficiency in Python and Advanced SQL.
• Experience with CI/CD pipelines and modern data tooling.
• Leadership track record in data architecture strategy.

Responsibilities

• Design, build, and optimize scalable data pipelines and data warehouse structures on GCP.
• Define standards for code quality, data modeling, CI/CD, testing, monitoring, and security.
• Provide technical direction and mentor mid-level and senior data engineers.
• Build batch and real-time data ingestion pipelines using BigQuery, Dataflow, Pub/Sub, and Dataproc.
• Implement workflow orchestration using Cloud Composer (Airflow) or dbt.
• Create scalable data models (Kimball, Star/Snowflake, Data Vault) for analytics.
• Implement data quality checks, lineage tracking, and monitoring.
• Partner with Analytics, Data Science, and DevOps teams for self-service data solutions.
• Ensure adherence to data governance policies and encryption, HIPAA/SOC 2 considerations.

Skills

Leadership

Data architecture

Mentoring

ETL/ELT design

Cross-functional collaboration

Analytical thinking

Tools

BigQuery

Dataflow

Pub/Sub

Dataproc

Cloud Composer

dbt

Terraform

Python

SQL

IAM & Security

Job description

We are seeking an experienced Lead GCP Data Engineer to design, architect, and scale our enterprise data infrastructure on Google Cloud Platform (GCP). In this role, you will lead the technical vision for our data environment, drive end-to-end ELT/ETL pipeline architecture, and mentor a team of engineers. You will collaborate closely with data architects, software engineers, data scientists, and business stakeholders to turn complex data into actionable business intelligence and AI-ready data products.

Key Responsibilities

Technical Leadership & Architecture

• Data Architecture: Design, build, and optimize scalable, reliable, and cost-efficient data pipelines and data warehouse structures on Google Cloud Platform.
• Team Governance & Standards: Define standards and best practices for code quality, data modeling, CI/CD, testing, monitoring, and security across the data engineering workflow.
• Mentorship: Provide technical direction, lead design reviews, and mentor mid-level and senior data engineers.
• Pipeline Development: Build robust batch and real-time streaming data ingestion pipelines using Google BigQuery, Dataflow (Apache Beam), Pub/Sub, and Dataproc.
• Orchestration: Implement automated workflow orchestration using Cloud Composer (Apache Airflow) or dbt.
• Data Modeling: Create scalable data models (Kimball, Star/Snowflake schemas, Data Vault) optimized for analytical query performance and cost governance in BigQuery.
• Quality & Monitoring: Implement automated data quality checks, data lineage tracking, alert frameworks, and proactive system health monitoring.
• Cross-Functional Strategy: Partner with Analytics, Data Science, and DevOps teams to deliver self-service data solutions and support advanced analytics/ML models.
• Security & Compliance: Ensure adherence to data governance policies, IAM best practices, encryption at rest/in transit, and compliance frameworks (GDPR, HIPAA, SOC 2).

Required Qualifications

• Experience: 8+ years of total data engineering experience, with 4+ years of hands‑on expertise leading GCP data implementations at enterprise scale.
• Core GCP Stack: Advanced proficiency in BigQuery, Cloud Composer, Dataflow, Pub/Sub, Cloud Storage, and Dataproc.
• Programming & SQL: Expert-level mastery of Python and Advanced SQL (window functions, query tuning, partitioning, clustering).
• Data Transformation & CI/CD: Hands‑on experience with dbt, Terraform (IaC), and modern CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins).
• Leadership: Proven track record of leading technical projects, mentoring engineers, and setting data architecture strategy.