Lead Data Engineer - Cloud Data Products & Analytic Enablement

highmarkhealth; United States; On-site; USD 150,000 - 190,000; Full time

Job summary

Highmark Health is seeking a Lead Data Engineer - Cloud Data Products & Analytic Enablement to transform diverse data sources into actionable intelligence for critical decisions across the organization and within our application ecosystem.

This role requires deep expertise in data engineering, analytical programming, and especially Google Cloud Platform technologies to design, build, and optimize scalable data pipelines with quality and governance at the core.

Qualifications

• 7 years of experience in design and analysis of algorithms, data structures, and design patterns in building and deploying scalable systems.
• 7+ years in data engineering, ETL development, or data management roles.
• 7 years of SQL experience with MySQL, PostgreSQL, or MongoDB.
• 7+ years with data warehouse concepts (e.g., Snowflake, Redshift, BigQuery).

Responsibilities

• Lead the design, development, and maintenance of data processes to move and transform data across systems.
• Oversee data models, databases, and warehouses to support BI and analytics needs.
• Collaborate with IT, product, analytics, and business teams to gather requirements.
• Mentor associate, intermediate, and senior data engineers as needed.
• Ensure consistency of data solutions across systems and platforms with other Data Leaders.
• Monitor production schedules, report progress, and escalate issues to lead developers.

Skills

SQL

Python

GCP

Airflow

BigQuery

Data modeling

dbt

Data governance

Stakeholder collaboration

Tools

Cloud Composer

BigQuery

DataFlow

DataProc

Cloud Run

DataPlex

dbt

Starburst/Trino

Job description

JOB SUMMARY

***CANDIDATE MUST BE US Citizen (due to contractual/access requirements)***

As a Lead Data Engineer - Cloud Data Products & Analytic Enablement, you will be instrumental in transforming diverse data sources into actionable intelligence, empowering critical business decisions across our organization and within our application ecosystem.

This pivotal role demands a strong foundation in data engineering principles, combined with a keen focus on analytical engineering to unlock the full potential of our data assets.

You will leverage your deep expertise in Google Cloud Platform (GCP) technologies, including Managed Airflow (Cloud Composer), BigQuery, Cloud Run Functions, DataFlow, DataProc, and DataPlex, to design, build, and optimize scalable data pipelines.

Your work will encompass the entire data lifecycle from ingestion and processing to modeling and orchestration, ensuring the highest standards of data quality, performance, and governance.

A core aspect of this role involves translating complex engineered data into readily consumable, business-ready insights. You will achieve this by developing curated datasets and robust analytical assets.

Success in this position requires exceptional proficiency in SQL and Python, coupled with the ability to strategically partner with diverse stakeholders, translating intricate business requirements into impactful, scalable data products.

ESSENTIAL RESPONSIBILITIES

• Lead the design, develop, and maintain robust data processes and solutions to ensure the efficient movement and transformation of data across multiple systems
• Oversee development and maintain data models, databases, and data warehouses to support business intelligence and analytics needs
• Collaborate with stakeholders across IT, product, analytics, and business teams to gather requirements and provide data solutions that meet organizational needs
• Mentor other associate, intermediate, and senior data engineers as needed
• Collaborate with other Data Leaders to ensure consistency of data solutions across systems and platforms
• Monitor work against the production schedule, provide progress updates, and report any issues or technical difficulties to lead developers regularly
• Implement and manage data governance practices, ensuring data quality, integrity, and compliance with relevant regulations.
• Stay current with industry trends and emerging technologies in data engineering, recommending new tools and best practices as needed
• Other duties as assigned or requested.

EXPERIENCE

Required

• 7 years of experience in design and analysis of algorithms, data structures, and design patterns in the building and deploying of scalable, highly available systems
• 7 years of experience in a data engineering, ETL development, or data management role.
• 7 years of experience in SQL and experience with database technologies (e.g., MySQL, PostgreSQL, MongoDB).
• 7 years of experience in with data warehouse solutions and concepts (e.g., Snowflake, Redshift, BigQuery)

Preferred

• 7+ years of experience in designing, developing, and meticulously implementing robust data solutions, encompassing sophisticated structuring and transformation of data from diverse, complex source systems.
• 7+ years of experience in producing high-quality, efficient, and maintainable data-related code that underpins critical stakeholder applications.
• 7+ years of experience working across a multitude of technology systems, consistently designing innovative solutions or developing impactful data solutions specifically within the complex healthcare or insurance industry.
• 7+ years of experience in strategically translating intricate business requirements, design mockups, prototypes, and user stories into precise technical designs and highly impactful, scalable data solutions.
• 7+ years of experience with both SQL and Python, utilizing these languages for sophisticated data manipulation, advanced analytical insights, and the development of resilient data pipelines.
• 5+ years of experience leveraging core GCP data services, including BigQuery, DataFlow, Cloud Composer (Managed Airflow), DataProc, and Cloud Run, optimizing their use for cloud-native data architectures.
• 5+ years of experience with traditional on-premise database systems, including Oracle, Teradata, and DB2, bridging legacy and modern data landscapes.
• 5+ years of experience operating within Unix/Linux environments, including scripting and system-level data operations.
• Possesses proven, hands-on experience with dbt (data build tool) for advanced data transformation and the development of sophisticated analytical data models.
• Experienced with data virtualization tools such as Starburst (Trino) or other composable data platforms, enabling unified access to distributed data assets.
• Skilled in leveraging advanced data quality engines (e.g., Monte Carlo, Soda) to ensure proactive data observability, integrity, and reliability across the data lands