Note: The job is a remote job and is open to candidates in USA. Applied Systems builds cloud software and AI-powered solutions for the global insurance industry. The Sr. Manager, Data Engineering will build and scale the company’s data foundation, lead a global team of data engineers, establish engineering standards, and drive data, DataOps, and AI adoption.
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
Skills
- 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
Benefits
- Medical, Dental, and Vision Coverage
- Holiday and Vacation Time
- Health & Wellness Days
- A Bonus Day for Your Birthday
Company Overview
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