Sr. Manager, Data Engineering at Applied Systems

Job description

Team: Corporate Data Services Salary Range: $135,000-$185,000 USD Location: Remote Opportunity for Impact Applied builds cloud software and AI-powered solutions that are reinventing how the global insurance industry operates, and we do it at a pace that keeps us ahead. What you ship here reaches thousands of agencies and brokers worldwide and sets the standard for what modern insurance technology looks like. Applied Systems, Inc., a worldwide leader in insurance technology, is currently searching for an experienced Sr. Manager, Data Engineering with proven experience in Data and DataOps to join our team. We are on an exciting journey to build and scale our Data and Analytics capabilities to enable better data-driven decision making and AI capabilities across the business. 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! What You’ll Do Team Leadership • 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. Data Pipelines and Integration • 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. 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. • 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. Reliability and DataOps • 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. Strategic Leadership • 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. What We’re Looking For • 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. Why You’ll Like Working on Team Applied The best work happens when smart people move fast, together. For over 40+ years, Applied has built technology that solves real problems for insurance professionals - and we’re still pushing what’s possible. You’ll be part of our people-first culture built on trust, inclusion, and growth - where you’re supported to deliver, collaborate with teammates who care, and grow with leaders invested in your success. How We’ll Support You We invest in the whole person, not just the role. Our benefits and resources are built to support your health, your time, and your life outside of work: • Medical, Dental, and Vision Coverage • Holiday and Vacation Time • Health & Wellness Days • A Bonus Day for Your Birthday Compensation Transparency Our targeted starting base salary in the United States for this position considers a variety of factors, including depth and breadth of experience, skills and role scope. Depending on the role, team members may also be eligible for additional compensation plans (bonus and commission). Your Security Matters: Our candidates’ personal information and online safety are top of mind. Applied communicates with candidates only via a secure @appliedsystems.com email address or through our official careers portal. Recruiters will never request payments or ask for financial account or sensitive personal information like Social Security numbers. AI Utilization We leverage AI tools to streamline parts of our recruitment workflow (such as resume parsing and interview scheduling). However, final decisions are always conducted by real humans. EEO Statement Applied Systems is proud to be an Equal Employment Opportunity Employer. Diversity and Inclusion is a business imperative and is a part of building our brand and reputation. At Applied, we don’t discriminate, and we are committed to recruit, develop, retain, and promote regardless of race, religion, color, national origin, sexual orientation, gender identity, disability, age, veteran status, and other protected status as required by applicable law. #LI-Hybrid / #LI-Remote #LI-US

Requirements

Must-haves

Team: Corporate Data Services
Location: Remote
Opportunity for Impact
What You’ll Do
Team Leadership
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.
Data Pipelines and Integration
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.
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.
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.
Reliability and DataOps

Nice-to-haves

A Bonus Day for Your Birthday

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aigcppythonsqlrestci/cdmlmachine learning

Platform: icims
Effort: medium

Benefits

Benefits detected in the listing.

Dental

Vision

Wellness