Purpose
We are seeking a Data Engineering Manager to lead a small, high-performing team responsible for delivering scalable, reliable, and cost-effective data staging and analytics solutions across our enterprise data ecosystem.
This role is accountable for the operational reliability, performance, and continuous improvement of enterprise data platforms that power business intelligence, reporting, and emerging advanced analytics use cases. While not an AI engineering role, the successful candidate will understand the data patterns and platform capabilities required to support machine learning and AI-driven initiatives as they arise.
You will oversee intake, estimation, prioritization, resourcing, and delivery while ensuring alignment with enterprise data architecture, governance standards, and long-term platform direction. The ideal candidate combines strong delivery leadership with sound engineering judgment and a pragmatic approach to technology decisions.
Our environment includes Snowflake for centralized analytics aggregation, alongside row-based platforms such as Postgres and cloud services (Azure/AWS). Success in this role requires the ability to balance these technologies effectively and deliver trusted, analytics-ready data efficiently.
Key Responsibilities
-
Lead, mentor, and develop a small team of data engineers
-
Establish clear delivery processes, including backlog management, sprint planning, and release coordination
-
Drive predictable execution, measurable outcomes, and continuous improvement
-
Define and track delivery KPIs including reliability, cycle time, defect rates, and platform stability
-
Oversee operational support, incident management, and structured root cause analysis for data pipelines
-
Own structured intake and estimation processes for new data initiatives
-
Partner with stakeholders to prioritize work based on business value, risk, and capacity
-
Manage team workload, resource allocation, and delivery timelines
-
Provide transparent status reporting and manage tradeoffs proactively
-
Ensure new initiatives align with enterprise architecture standards, governance requirements, and long-term platform sustainability
-
Deliver curated datasets and staging solutions across:
-
Snowflake for centralized analytics and aggregated data layers
-
Postgres and Azure/AWS data services
-
Design and implement robust ETL/ELT pipelines supporting scalable, reliable data ingestion and transformation
-
Implement version control, automated testing, and CI/CD practices for data pipelines
-
Optimize workload placement to balance cost, performance, scalability, and maintainability
-
Ensure data is delivered in structured, analytics-ready formats for BI, reporting, and advanced analytics consumers
-
Design data models and pipelines capable of supporting semi-structured data and future advanced analytics or AI use cases
-
Contribute to the evolution of the enterprise data platform by identifying improvements in tooling, patterns, and architecture
-
Promote standardized pipeline patterns, reusable components, and shared curated datasets
-
Support development of domain-aligned, reusable data assets that can be leveraged across teams
-
Reduce duplication and improve maintainability across data assets
-
Drive cost-awareness and optimization across compute, storage, and transformation layers
-
Implement data quality monitoring, observability, and lineage practices
-
Establish measurable SLAs for critical datasets and ensure transparency of performance metrics
-
Partner closely with the Enterprise Data Architect to translate target-state models into implementable schemas and pipelines
-
Support execution of the enterprise data strategy through aligned engineering practices and delivery
-
Ensure engineering work aligns with enterprise standards, reference architectures, and change governance processes
-
Maintain strong documentation practices including architectural decisions, runbooks, and operational procedures
-
Support metadata management, data cataloging, and practices that improve data discoverability and trust
-
Contribute input into platform and architecture decisions based on operational insights and delivery experience
-
Predictable and reliable delivery of data engineering initiatives
-
Reduced duplication through standardized patterns and reusable data assets
-
Measurable improvements in pipeline reliability, data quality, and operational stability
-
Improved performance and cost efficiency across Snowflake and OLTP platforms
-
Clearly defined SLAs for critical datasets with transparent reporting
-
Strong collaboration with architecture and business stakeholders
-
Data platforms that are scalable, sustainable, and adaptable to support future advanced analytics and evolving business needs
Key Competencies and Skills Required
-
Strong SQL skills and hands-on experience building and operating production data pipelines
-
Experience designing and operating modern ETL/ELT pipelines using tools such as Talend or comparable platforms
-
Working experience with Snowflake or similar cloud data warehouse platforms
-
Experience with Postgres or comparable relational databases and sound judgment on workload placement decisions
-
Experience implementing data testing, monitoring, and observability practices
-
Working knowledge of data patterns that support advanced analytics or AI workloads, including semi-structured data handling and reproducible data pipelines
-
Demonstrated proficiency using AI-assisted engineering tools (e.g., code generation, query optimization, documentation, and debugging) to improve productivity while maintaining quality, security, and governance standards
-
Proven experience managing intake, estimation, prioritization, and stakeholder expectations
-
Strong communication skills with the ability to translate technical concepts for business audiences
Minimum Qualifications
- Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience
-
8+ years of experience in data engineering, data warehousing, or analytics engineering with demonstrated ownership of production-grade data platforms
-
3+ years of experience leading or managing a small engineering team
Preferred Qualifications
-
Hands-on experience with Talend (Talend Cloud and/or Remote Engine) in an enterprise environment
-
Experience working in hybrid cloud and on-prem data ecosystems
-
Familiarity with architecture and change governance processes
-
Experience implementing data quality monitoring, metadata/lineage practices, or data catalog tools
-
Experience supporting data pipelines for enterprise BI platforms such as Power BI
-
Exposure to data preparation practices that support machine learning or predictive analytics initiatives
-
Familiarity with modern data platform automation and CI/CD practices
Our commitment to excellence in customer service is only part of our story. We are also dedicated to supporting our most valuable asset, our associates! One of the ways we do this is by offering a variety of high-quality benefits for our associates and their families.
All full-time associates receive the benefits of law and are eligible for the following benefits:
-
Mayor Medical Insurance for Family
-
Life insurance
-
Food Coupons
-
10 days of vacation
-
30-day year end bonus
We're an equal opportunity employer and we welcome diversity and inclusion! Reece USA is an Equal Opportunity Employer— Employer Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, and any other status protected by law.
