Senior Manager, Data Engineering

Jobgether; United States; Remote; USD 180,000 - 240,000; Full time

Benefits offered by this job

Remote work in the United States

Unlimited PTO

Healthcare: medical, dental, vision

Paid parental leave

Retirement plans

Promote-from-within culture

Job summary

Jobgether in the United States seeks a Senior Manager of Data Engineering to lead a modern data platform effort. The role blends strategic leadership with hands-on engineering, guiding a team of data engineers and shaping architecture for trusted data across the customer journey.

The position focuses on building reliable data pipelines, consistent metrics, and secure APIs, collaborating with Finance, Operations, Sales, Marketing, and Product to translate evolving business needs into scalable

Qualifications

• 10+ years in software or data engineering, with 5+ years managing engineers.
• Proven record of recruiting, developing talent, and building effective teams.
• Experience designing enterprise data architectures, governance, and data modeling.
• Hands-on with modern data warehouses and transformation frameworks (Snowflake, BigQuery, dbt).
• Proficient in at least one backend language (Python) and code review.
• Experience delivering large data platform projects across teams.
• Knowledge of API security, data privacy, and PII handling.
• Experience preparing trusted data for AI consumers with traceability.
• Vendor evaluation, contract negotiation, and cost management.
• Strong stakeholder management with Finance, Capital Markets, and Accounting.

Responsibilities

• Own the team’s technical direction, delivery, and platform outcomes.
• Define and drive a data engineering roadmap aligned with business objectives.
• Architect data ingestion, warehousing, modeling, and governance.
• Build reliable data pipelines and defined metric definitions.
• Develop secure data access APIs with proper authorization and privacy controls.
• Prepare data for AI agents and ensure traceability to sources.
• Promote agentic engineering practices across the team.
• Manage vendors and tooling costs within approved limits.
• Collaborate with Finance, Operations, Sales, Marketing, and Product to meet data needs.
• Balance quality, speed, cost, and business impact in prioritization.

Skills

People leadership

Enterprise data architecture

Python

Agentic engineering

Data governance

Cross-functional collaboration

Vendor management

Communication & strategic judgment

Background check willingness

Tools

Snowflake

BigQuery

dbt

Job description

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

The Senior Manager, Data Engineering will lead the development of a modern data platform that makes trusted, governed information accessible to product teams, business stakeholders, and AI agents. This player-coach role combines strategic leadership with hands‑on technical expertise, shaping architecture while guiding a team of skilled data engineers. You will build reliable data pipelines, consistent metrics, and secure APIs that connect information across the entire customer journey. Working closely with Finance, Operations, Sales, Marketing, and Product, you will translate evolving business needs into scalable data solutions. You will also advance AI‑enabled engineering practices and ensure that data consumed by AI systems remains traceable, secure, and reliable. This is an opportunity to influence the organization’s data strategy while developing people, improving operational efficiency, and enabling better business decisions.

Accountabilities

As Senior Manager, Data Engineering, you will own the team's technical direction, delivery, and overall platform outcomes.

• Lead and develop the team: Recruit, coach, and retain talented data engineers. Set clear expectations, provide constructive feedback, establish career development paths, and build a collaborative team that can operate effectively without relying on individual contributors.
• Define the roadmap and deliver results: Translate business objectives into a prioritized data engineering roadmap, communicate trade-offs, establish delivery expectations, and lead cross‑functional initiatives on time and within budget.
• Own data architecture: Establish and evolve the architecture for data ingestion, warehousing, modeling, semantic layers, lineage, and governance. Lead architecture reviews, maintain technical standards, and contribute directly to implementation when needed.
• Build reliable data pipelines: Ensure data pipelines meet defined service‑level agreements, maintain consistent metric definitions, and provide dependable information for reporting, products, and AI‑powered applications.
• Develop secure data access capabilities: Oversee the design and implementation of the platform's serving layer and APIs, establishing standards for authorization, access control, privacy, and the secure handling of personally identifiable information.
• Prepare data for AI agents: Build governed, agent‑ready data capabilities that allow AI systems to retrieve trustworthy information and produce answers that can be traced back to their original sources.
• Advance agentic engineering practices: Use coding agents in your own engineering workflow and introduce effective agentic development practices across the team to improve productivity and delivery.
• Manage vendors and costs: Evaluate data platform providers and engineering tools, negotiate contracts within delegated approval limits, and monitor warehouse and tooling expenses to maintain cost efficiency.
• Partner across business functions: Collaborate with Finance, Operations, Sales, Marketing, Product, Accounting, and Capital Markets stakeholders to understand data needs, build consensus, and recommend practical solutions.
• Drive continuous improvement: Balance technical quality, delivery speed, cost, and business impact while making deliberate decisions about priorities and determining which initiatives should not be pursued.

Requirements

The ideal candidate combines substantial software and data engineering experience with strong people leadership, enterprise architecture expertise, and a practical understanding of AI‑enabled engineering. Key qualifications include:

• Professional experience: At least 10 years of experience in software engineering or data engineering, including 5 or more years managing engineers.
• People leadership: A proven record of recruiting strong engineers, developing talent, building effective teams, and establishing clear accountability and career growth opportunities.
• Enterprise data architecture: Deep experience designing and leading enterprise data architectures, including data warehouses, data modeling, lineage, governance, and architecture review processes.
• Modern data technologies: Hands‑on experience with modern data warehouses and transformation frameworks such as Snowflake, BigQuery, and dbt, alongside strong SQL skills.
• Backend engineering: Working proficiency in at least one backend programming language, such as Python, with the ability to review code, contribute technically, and guide implementation decisions.
• Agentic engineering: Demonstrated experience using coding agents beyond basic prompting and introducing agentic engineering practices into a team's development workflows.
• API security and data privacy: Practical knowledge of production APIs, authorization mechanisms, access controls, and the secure handling of personally identifiable information.
• Data platform delivery: A track record of delivering complex data platform projects across multiple teams, prioritizing work effectively, managing trade‑offs, and making sound decisions about what not to build.
• Governed data for AI: Experience preparing trusted, governed datasets for AI consumers, with reliable traceability from generated answers or outputs to their source data.
• Cross‑functional collaboration: Experience building consensus and working effectively with stakeholders outside Engineering, particularly in Finance, Capital Markets, and Accounting.
• Vendor management: Experience evaluating technology vendors, negotiating contracts, and managing platform or tooling costs.
• Communication and strategic judgment: Strong communication, decision‑making, and stakeholder management skills, with the ability to connect technical investments to measurable business outcomes.
• Background screening: Willingness to complete a background check as a condition of employment.

Benefits

• Remote work: Remote position based in the United States.
• Paid time off: Unlimited PTO.
• Healthcare: Medical, dental, and vision coverage.
• Parental leave: Paid parental leave.
• Retirement: Retirement plans.
• Career development: A promote‑from‑within culture that emphasizes talent development and career growth.
• Purpose‑driven work: The opportunity to contribute to a cleaner, more accessible, and reliable home energy future.
• Collaborative culture: A working environment focused on collaboration, meaningful impact, and work‑life balance.

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!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre‑contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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.