Data Steward, Re:Cycle Reverse Logistics

The re:Cycle Reverse Logistics Operations team offers worldwide cloud computing providers with a centralized means to disassemble, sort, function test, in-warranty return, repair, erase/wipe, and disposition server and networking assets that break or are no longer needed in the fleet. The team is seeking a detail-oriented, analytical, and self-driven Data Steward to support its global operations by driving and working collaboratively across teams to maintain data consistency and accuracy, and by generating and driving data-related performance metrics in the organization’s warehouse management system. This scope will ensure data reliability for the organization’s three operating systems, driven by 400+ global users through our four global facilities that are generating millions of transactions per month across 70+ data tables. This role is critical to the re:Cycle Reverse Logsitics system-management team that is responsible for maintaining application security, data, and compliance, and plays a key role in enabling global operations with reliable information needed to align on daily, monthly, and annual plans, and tracking operational performance to plan. Auditing and analyzing the system’s data (both extracted and in-application), repairing inaccurate data, working across the organization and 3P service provider to root cause and repair recurring data issues, analyzing and prioritizing data "bugs" for resolution, supporting data change management initiatives, and generating/driving data-related performance metrics for leadership presentation are all in a day’s work for the reverse logistics Data Steward.

Key job responsibilities

Own accuracy, consistency, and completeness of source of truth data across the organization's warehouse management systems

Design and maintain recurring SQL based audit mechanisms that validate data accuracy between source systems and the downstream data warehouse

Root cause data discrepancies, triage and prioritize incidents by business impact, and drive resolution through the system's 3P service provider and internal engineering

Define and track data quality health metrics; develop scorecards and system health reports for stakeholders and QBRs

Develop, formalize, and communicate data standards, policies, and governance strategies; prove compliance against those policies

Identify reporting gaps between source and downstream systems; collaborate with data engineering to influence architecture, automate risk identification, and enable new system data

Validate data accuracy in staging environments ahead of production release in collaboration with Vendor Product Managers

A day in the life

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About the team

The re:Cycle Reverse Logistics Operations team offers worldwide cloud computing providers with a centralized means to disassemble, sort, function test, in-warranty return, repair, erase/wipe, and disposition server and networking assets that break or are no longer needed in the fleet. The Operation Integration team supports global operations by enabling and maintaining the warehouse management systems and processes that govern every value-add service that the organization performs on millions of parts per year. This team is critical in providing software guardrails to protect assets, to ensuring traceability of assets coming/going, and providing cross-organizational data.

Basic qualifications

- Experience working with Microsoft Office tools (Excel, Word, Outlook, etc.)

- 3+ years of experience in data management, data quality, data governance, or a related role within a warehousing, supply chain, manufacturing, or reverse logistics environment

- Bachelor's degree in Data Science, Computer Science, Information Systems, or related field - OR 4+ additional years of related experience in lieu of a degree

Preferred qualifications

- Hands on SQL experience in a data warehouse environment (Redshift, Snowflake, BigQuery, or similar), including window functions, CTEs, and cross database queries

- Experience designing or maintaining data quality audit mechanisms, validation frameworks, or automated monitoring scripts

- Experience with ETL/ELT pipelines, data warehouse technical architectures, data modeling, and data flow from source systems to consumption layers

- Experience authoring data governance policies, data standards, or compliance documentation

- Experience managing a third party vendor relationship for technical root cause and resolution

- Familiarity with AWS data stack (Redshift, S3, Glue, or similar cloud data infrastructure)

- Experience defining data quality SLAs, KPIs, acceptance criteria, or incident prioritization frameworks based on consumer input

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