Senior Data Engineering Manager/Coach

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Data Engineering Manager/Coach based in United States.

This is a senior leadership role focused on building and scaling enterprise-grade data platforms that support large-scale healthcare transformation.
You will lead a team of data engineering professionals while shaping platform architecture, engineering standards, and long-term data strategy.
The role combines hands-on technical leadership with people management, financial accountability, and cross-functional partnership.
You will help turn complex and high-volume healthcare data into reliable, accessible platforms that enable better analytics and evidence-based decisions.
Modern technologies such as Databricks, Snowflake, Spark, Airflow, dbt, Kafka, and cloud data services will be central to the platform environment.
Success will depend on balancing data quality, scalability, security, performance, infrastructure costs, and business value.
This fully remote U.S. opportunity offers meaningful public-sector impact, with approximately 10% travel to Sacramento for key meetings and working hours aligned with Pacific Time.

Accountabilities:

As the Senior Data Engineering Manager/Coach, you will own the evolution of the data platform while developing a high-performing engineering organization. You will be responsible for technical direction, operational excellence, business alignment, and the growth of your team.

• Define data platform strategy and make architecture decisions across enterprise data systems, pipelines, warehouses, and lake architectures.
• Design and oversee scalable batch and real-time data solutions while balancing freshness, accuracy, reliability, performance, and infrastructure costs.
• Establish engineering standards for data quality, governance, documentation, monitoring, security, and compliance.
• Own platform budgets, cloud data costs, tooling investments, resource planning, and ROI analysis for data initiatives.
• Translate data engineering investments into measurable business value and communicate technical trade-offs clearly to stakeholders.
• Manage, mentor, and develop a team of approximately 10–20+ data engineers through regular coaching, career planning, performance management, hiring, and succession development.
• Build an inclusive, collaborative engineering culture centered on automation, data quality, continuous learning, and technical excellence.
• Partner with Analytics, Data Science, Business Intelligence, Product Management, and Software Engineering teams to define requirements and prioritize data products.
• Lead technical interviews, hiring decisions, onboarding, skills development, and stretch assignments to strengthen the team's long-term capabilities.
• Drive continuous improvement across ETL/ELT processes, platform tooling, data reliability, pipeline performance, and operational efficiency.
• Support platform migrations and technology adoption while guiding teams through organizational and technical change.

Requirements

The role requires a combination of senior data engineering leadership, strong technical depth, business and financial acumen, and a coaching-oriented management style. You should be comfortable operating at both strategic and technical levels while developing teams and communicating with technical and non-technical stakeholders.

• Proven experience managing large data engineering teams, ideally 20+ members.
• Experience owning budgets, P&L responsibilities, or financial accountability for data platforms or technology products.
• Demonstrated ability to connect data infrastructure investments with business outcomes, KPIs, and ROI.
• Experience building and operating production-scale data platforms across the full data lifecycle, from ingestion through consumption.
• Strong understanding of modern data engineering practices, cloud data technologies, data architecture, governance, and quality management.
• Experience making architectural decisions involving data pipelines, platforms, warehouses, lakes, and streaming systems.
• Hands-on knowledge of platforms such as Snowflake, Databricks, BigQuery, Redshift, or comparable technologies.
• Experience with Apache Spark, Airflow, dbt, Kafka, and real-time or streaming architectures.
• Familiarity with AWS, Azure, or GCP data services and infrastructure-as-code practices.
• Strong understanding of data modeling approaches such as dimensional modeling, data vault, and data mesh principles.
• Proficiency with SQL, Python, Scala, or comparable data-focused programming technologies.
• Strong financial and operational skills, including cloud cost optimization, capacity planning, vendor evaluation, and resource management.
• Excellent communication skills with the ability to explain technical architecture and investment decisions in clear business terms.
• Demonstrated success in performance management, career development, hiring, team building, and difficult conversations.
• Strong strategic thinking, problem-solving, decision-making, collaboration, mentorship, and change-management capabilities.
• A consulting mindset, high ownership, curiosity, and motivation to solve complex organizational and technical challenges.
• Bachelor's degree in Computer Science, Engineering, or equivalent professional experience.
• Must be authorized to work in the United States; visa sponsorship or transfer is not available for this role.
• Ability to work Monday through Friday on Pacific Time and travel to Sacramento approximately 10% of the time.

Benefits

• Fully remote position within the United States.
• Full-time, long-term consulting engagement.
• Opportunity to work on large-scale healthcare data transformation with significant public-sector impact.
• Choice of engagement structure, including W2 employment or 1099/independent contractor arrangements.
• Opportunity to lead and coach a substantial data engineering organization.
• Exposure to modern cloud data platforms, analytics technologies, streaming architectures, and enterprise-scale data environments.
• Significant ownership over data platform strategy, architecture, engineering standards, and investment decisions.
• Regular opportunities to influence cross-functional initiatives spanning data engineering, analytics, data science, BI, product, and software engineering.
• Approximately 10% travel to Sacramento for key meetings.
• Working schedule aligned with Pacific Time, Monday through Friday.
• Benefits and compensation details may vary depending on the selected engagement structure.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.