Manager - Data Integration

The core responsibilities for the job include the following:

Team Leadership:

Lead and mentor a growing team of data engineers, including talent reviews and career development.

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 and 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, and maintain ETL/ELT pipelines from enterprise applications, internal services, and third-party APIs.

Design, develop, and operationalize 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.

Drive AI adoption across the team's engineering workflowsthe team has a mandate for AI adoption, and you'll be expected to be a role model, champion, remove friction, and help engineers integrate AI tools into their daily development, code review, documentation, and debugging practices.

Design and oversee data models in a medallion architecture.

Mandate high standards for data validation, profiling, reconciliation, and quality initiatives.

Stay updated with industry trends and technologies to continuously improve our data engineering practices.

Reliability and DataOps:

Build monitoring and alerting for data jobs, orchestration, and lakehouse health.

Participate in and evolve production support, incident response, and on-call rotations.

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.

Requirements:

3+ years of leading or managing data engineering teams with an emphasis on data analytics, DevOps, and modern data platforms.

5+ years of hands-on data engineering experience in cloud environments.

Direct experience in the design and development of large-scale data solutions using GCP services like Dataproc, Dataflow, Cloud Bigtable, BigQuery, Cloud SQL, Pub/Sub, Cloud Data Fusion, Cloud Composer, Cloud Functions, Cloud Storage, Compute Engine, Looker, and Cloud IAM.

Experience in implementing cloud data solutions in the context of business applications, cost optimization, business strategic needs, and future growth goals as it relates to becoming a data-driven organization.

Expert-level knowledge of architecture frameworks, methodologies, and tools.

Solid working understanding across all the disciplines within a data team: Data Visualization, Data Governance, Artificial Intelligence, and Machine Learning.

Experience implementing Infrastructure as Code (IaC), about automating Cloud IAM and Data Policy Tags.

Excellent communication skills and the ability to articulate technical concepts to non-technical stakeholders.

Expert-level knowledge of data modeling.

Strong execution habits: you create and maintain project timelines and know when things are off track before your team tells you.

A proactive mindset toward AI-assisted engineering; you should already be using AI tools (Copilot, Claude, ChatGPT, or similar) in your own work and have opinions about how they change engineering workflows, code quality, and team productivity. We're looking for someone who sees AI as a multiplier.