Senior Manager, Data Engineering

Accountabilities: As a Senior Manager, Data Engineering, you will own the evolution of a scalable data ecosystem while leading engineering teams and partnering with business stakeholders. You will be responsible for improving platform reliability, enabling self-service analytics, and ensuring data products deliver measurable value across the organization.

Lead, mentor, and grow a high-performing team of data engineers by establishing strong engineering practices, supporting career development, and fostering a culture of ownership and continuous learning.

Own the reliability, quality, scalability, and operational excellence of core data platforms, including ingestion, transformation, orchestration, and serving layers.

Establish strong data quality standards through lineage tracking, freshness monitoring, anomaly detection, observability practices, and reliable performance metrics.

Drive the development of self-service analytics capabilities that reduce manual requests and empower teams to access trusted data independently.

Manage platform efficiency by optimizing compute and storage costs while maintaining high reliability and performance standards.

Partner with Data Science, Analytics, Product, Engineering, and business teams to define data models, semantic layers, governance practices, and data contracts.

Promote engineering excellence through code reviews, testing strategies, CI/CD practices, incident response processes, and post-incident improvements.

Encourage responsible adoption of AI-powered engineering tools to improve productivity, quality, and team effectiveness.

Support strategic planning by translating business needs into scalable technical solutions and long-term platform improvements.

Requirements:

The ideal candidate is an experienced engineering leader with a strong background in data infrastructure, scalable systems, and team management. You bring technical depth, business awareness, and the ability to collaborate effectively across multiple functions.

8+ years of experience in data engineering, backend engineering, or data infrastructure roles, preferably within large-scale environments.

3+ years of experience managing engineering teams, including hiring, coaching, performance management, and team development.

Strong expertise in building and operating large-scale batch and streaming data pipelines using technologies such as Spark, dbt, Airflow, or similar orchestration frameworks.

Experience working with modern data platforms such as Databricks, Snowflake, BigQuery, or comparable lakehouse and warehouse technologies.

Proven ability to own data reliability initiatives, including SLAs, observability, lineage, quality monitoring, and incident management.

Strong understanding of data modeling principles, semantic layers, governance, privacy, and access-control practices.

Ability to design scalable data products that support multiple stakeholders and business use cases.

Excellent communication skills with the ability to align technical and non-technical teams around shared goals.

Experience building self-service data platforms or reducing operational dependencies through automation and reusable solutions is preferred.

Familiarity with AI-assisted engineering tools and experience applying AI responsibly within development workflows is a plus.

Strong problem-solving abilities, adaptability, ownership mindset, and the ability to thrive in a fast-changing environment.

Benefits:

Competitive compensation package based on experience and location.

Salary range for eligible US locations: approximately $180,200 - $274,300 USD annually depending on compensation zone.

Eligibility for company bonus programs and equity participation through Restricted Stock Units (RSUs).

Flexible remote-first work environment designed around autonomy and collaboration.

Opportunity to work with a global engineering organization building technology used at massive scale.

Supportive culture focused on growth, innovation, and continuous learning.

Opportunities to leverage modern technologies, including AI-powered tools, to improve engineering impact.

Team gatherings, offsites, and intentional opportunities for in-person collaboration.

Comprehensive employee benefits and wellness support.

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.

#LI-CL1