We are seeking an experienced Data Engineering Manager to lead the team responsible for the infrastructure and reliability of all DataNet jobs that extract, transform, and deliver data from Accounts Payable transaction processing systems. These data pipelines power the dashboards and reports that Operations teams rely on for daily execution, decision-making, and compliance. You will independently own the end-to-end data architecture, drive operational excellence across pipeline health and data quality, and lead a team of data engineers through the full software lifecycle — from design and implementation to monitoring and maintenance. You will define the 3 years strategic vision for the data platform, work backwards from the needs of operations stakeholders, and build mechanisms that ensure timely, accurate, and reliable data delivery at scale. Critically, you will champion the adoption of AI and machine learning technologies — including generative AI — to transform how data pipelines are built, monitored, and consumed, driving intelligent automation, predictive anomaly detection, and AI-powered insights that elevate the value of the data platform beyond traditional reporting. As a people leader, you will hire, develop, and coach a high-performing team, establish engineering best practices, and partner cross-functionally with AP transaction processing teams and operations consumers to continuously improve the data products that drive daily business outcomes.
Key job responsibilities
Own the end-to-end infrastructure and reliability of all DataNet jobs pulling data from AP transaction processing systems.
Define and execute the 3 years data architecture roadmap, making team-wide architectural decisions aligned with the broader AP Tech strategy.
Establish SLAs, monitoring mechanisms, and audit metrics for pipeline health, latency, data completeness, and accuracy.
Drive root cause analysis for pipeline failures and build automation to reduce operational toil and improve system resilience.
Partner with AP transaction processing teams to manage upstream data changes, schema evolution, and system migrations.
Work closely with Operations teams to ensure dashboards and reports meet their daily execution and decision-making needs.
Champion the adoption of AI/ML and generative AI technologies to enable intelligent pipeline automation, predictive anomaly detection, and AI-powered data insights.
Evaluate and implement emerging AI capabilities (e.g., auto-healing pipelines, natural language querying, AI-driven data quality monitoring) to future-proof the data platform.
Independently lead a team of data engineers — hire, develop, coach, and performance-manage across multiple levels (up to L5/L6).
Foster a psychologically safe, inclusive team environment; model Leadership Principles and delegate to build future leaders.
Write narratives and strategic documents; participate in OP1/OP2 planning; own the team's charter, vision, and tenets.
Tackle complex problems of up to 15-engineer scope — triage, break down, plan, and execute solutions; escalate effectively when necessary.
Drive simplification of delivery processes; reduce repeated work, remove defects, and structure efforts to avoid classic failure modes.
Basic Qualifications
- 2+ years of processing data with a massively parallel technology (such as Redshift, Teradata, Netezza, Spark or Hadoop based big data solution) experience
- 2+ years of relational database technology (such as Redshift, Oracle, MySQL or MS SQL) experience
- 2+ years of developing and operating large-scale data structures for business intelligence analytics (using ETL/ELT processes) experience
- 5+ years of data engineering experience
- Experience managing a data or BI team
- Experience communicating to senior management and customers verbally and in writing
- Experience leading and influencing the data or BI strategy of your team or organization
- Experience in at least one modern scripting or programming language, such as Python, Java, Scala, or NodeJS
Preferred Qualifications
- Experience with big data technologies such as: Hadoop, Hive, Spark, EMR
- Experience with AWS Tools and Technologies (Redshift, S3, EC2)
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