Sr. Manager, Data Engineering

Applied Systems, Inc.•,

5d•$135,000 - $185,000•Remote

About The Position

Applied builds cloud software and AI-powered solutions that are reinventing how the global insurance industry operates. This role is about building the data foundation Applied's business will run on. You will not be inheriting a mature platform and maintaining it — you will be standing much of it up, making the architectural calls, and setting the standards that everything built afterward depends on. You will write code, set engineering standards, and drive delivery, while leading and growing our global team of Data Engineers. We move quickly, priorities evolve as the business learns, and the person in this seat needs to find that energizing rather than unsettling. If you possess a curious and proactive personality, and you are passionate about data solutions that drive direct business outcomes, we want to hear from you!

Requirements

3+ years leading or managing data engineering teams, plus 5+ years hands-on data engineering in cloud environments.
Deep hands-on GCP experience — BigQuery, Cloud Composer/Airflow, dbt, Python and SQL — with working knowledge of the broader stack (Dataflow, Pub/Sub, Cloud Storage, Looker, Cloud IAM) and Infrastructure as Code for automating IAM and Data Policy Tags.
Expert level knowledge of data modeling and architecture frameworks, and experience balancing design decisions against cost, business needs, and future growth.
Solid working understanding across all the disciplines within a data team — Data Visualization, Data Governance, Artificial Intelligence and Machine Learning.
Strong execution habits: you create and maintain project timelines, and know when things are off track before your team tells you.
Excellent communication skills — you can articulate technical concepts to non-technical stakeholders and work effectively with US-based partners across time zones.
You already use AI tools (Copilot, Claude, ChatGPT, or similar) daily in your own engineering work, and have informed opinions about how they change workflows, code quality, and team productivity.
You have brought other engineers along, or are eager to — team-level adoption is a leadership problem as much as a tooling one.
You hold a clear line on accountability: AI-assisted output still gets reviewed, tested, and owned by a person.
You are energized by ambiguity, comfortable deciding with incomplete information, and you sequence work so value lands early rather than at the end of a long build.
You have built foundational platforms before, not only operated established ones, and you stay effective when priorities shift.
Talent shows up in a lot of different ways, and we mean that. We welcome candidates from all backgrounds and experience levels, including military members and their spouses and those without a traditional degree or tech background. If this role speaks to you, apply.

Nice To Haves

AI-First Engineering: This team has an explicit mandate for AI adoption. We are looking for someone already working this way who can bring a team along, not someone curious about getting started.

Responsibilities

Lead and mentor a growing team of data engineers, including talent reviews and career development.
Grow the team — own the hiring loop, and design onboarding, so new engineers contribute in weeks rather than months.
Own planning, estimation, prioritization, and delivery tracking that aligns with leadership direction and expectations.
Coordinate intake and stakeholder communication for data requests and roadmap planning.
Set and enforce compliance with architecture standards, engineering standards for code quality, testing, documentation, and production readiness.
Foster a culture of curiosity and continuous learning, where engineers explore new technologies, share knowledge, and question assumptions.
Design, build, operationalize and maintain robust and scalable data pipelines from enterprise applications, internal services, and third-party APIs that support business needs.
Lead in designing and building production data pipelines from data ingestion to consumption using GCP services, Python, BigQuery, dbt, SQL, Apache Airflow, Celigo etc.
Design and oversee data models in a medallion architecture.
Set high standards for data validation, profiling, reconciliation, and quality initiatives.
Stay updated with industry trends and technologies to continuously improve our data engineering practices.
Drive AI adoption across the team's engineering workflows — be a role model, champion it, remove friction, and help engineers integrate AI tools into their daily development, code review, documentation, and debugging practices.
Define the quality bar for AI-assisted work — what review, testing, and human judgement any AI-generated code or documentation must clear before it ships.
Measure and report the impact of AI adoption on delivery speed, quality, and engineer experience, so investment decisions rest on evidence.
Build data that is AI-ready by design: well-modeled, well-documented, and reliable enough for agents and models to consume without a human verifying every answer.
Define and own SLAs for critical datasets — freshness, completeness, and accuracy expectations that consumers can plan around.
Build monitoring and alerting for data jobs, orchestration, and lakehouse health.
Participate in and evolve production support, incident response, and on-call rotations.
Mature CI/CD, environment management, and release practices for data and analytics code.
Translate business goals into scalable data and automation solutions in partnership with both business and technology stakeholders.
Champion data democratization and self-service access to data and analytics across the company.
Evaluate and recommend tools and end-to-end solutions across Analytics, Data Engineering, ML Engineering and Data Governance.
Apply systems thinking to identify underlying problems and/or opportunities.
Partner closely with data governance so what we build is documented, owned, and trustworthy by design.