Manager, Data Engineering

Cornerstone Building Brands•Cary, NC

1d•$145,000 - $165,000•Onsite

About The Position

We're looking for a hands-on data engineer to own, maintain and expand the data infrastructure behind our Siding & Accessories commercial analytics function. This is not a business analyst role that touches data occasionally — it's a technical role that puts you in the database every day, writing queries, building and maintaining pipelines, curating the master and reference data everything joins to, and guaranteeing the integrity of the data that sales, product, customer, and market reporting depend on. You will own the commercial database from day one. You'll manage the SQL Server environment, keep data flowing cleanly from our source systems into the models and dashboards the business runs on, and build the pipelines that bring together sales, product, customer, and external market data into one trusted commercial dataset. Just as important, you'll own the master and reference data — the customer, product, and territory definitions everything reconciles to — so the numbers tie out no matter who's asking. If you've owned a production database, built and troubleshot data pipelines end to end, and can write SQL in your sleep, this role is for you.

Requirements

7+ years of hands-on SQL Server experience.
Hands-on experience with JD Edwards (JDE).
Proficiency with Python for data work.
Experience managing and maintaining a production database.
Strong understanding of data flow and integration between systems.
Experience integrating data from multiple sources.
Experience owning master or reference data.
Experience with data validation and quality assurance.
Independent problem-solving and operational ownership.
Experience leading or contributing to a data platform build or migration.
Experience with ETL tools or integration platforms (SSIS, Azure Data Factory, Boomi, Informatica, or similar).
Experience ingesting external / third-party data (industry, market, competitive, or share data) via APIs, purchased datasets, or scheduled files.
Familiarity with Power BI (or Tableau) and an understanding of what downstream reporting needs from a clean data model.
Exposure to master data management (MDM) or data governance practices — stewardship, reference-data versioning, or maintaining a canonical set of business definitions.
Experience with cloud data platforms (Snowflake, Azure, AWS).
Experience in manufacturing, building products, or B2B distribution industries.
Familiarity with R or other analytics/scripting languages beyond Python.

Responsibilities

Own and maintain the commercial SQL Server database — tables, views, stored procedures, data integrity, and performance.
Write, optimize, and troubleshoot SQL queries daily. This is hands-on work, not oversight.
Own the end-to-end data flow: source systems (JDE and others) → SQL Server commercial database → Power BI. Ensure data moves correctly, transformations are accurate, and outputs are validated before they reach stakeholders.
Build and maintain ingestion for external, third-party market data (industry, competitive, and share sources) and join it cleanly to internal sales, product, and customer data — reconciling keys, grain, and definitions across sources.
Monitor and respond to production incidents independently — pipeline failures, file failures, data quality issues. You triage, engage the right teams, and communicate status. You don't wait for someone to tell you what to do.
Own the data validation and quality assurance process. If data goes out wrong, it's on you to catch it first.
Own the master and reference data behind commercial reporting — customer, product, and sales-hierarchy / territory (e.g., TSM / RSM) masters — so every downstream model joins to one consistent, trusted set of definitions.
Build and maintain the crosswalks and mapping tables that reconcile codes and hierarchies across source systems, and between internal data and external market data.
Standardize, de-duplicate, and validate reference data, and enforce clear definitions and naming so the same entity means the same thing everywhere it's used.
Manage changes to master and reference data with an eye to history — version or effective-date changes so a mapping update doesn't silently rewrite prior reporting.
Act as data steward for these datasets: document what they are, who owns them, and how they refresh, and be the point of contact when a definition or hierarchy is in question.
Design, build, and expand data pipelines across the four commercial domains — sales, market, product, and customer — as reporting and analytics needs grow.
Stand up and operationalize ingestion for external market data feeds (APIs, purchased datasets, scheduled files), including refresh cadence, error handling, and lineage.
Document and reverse-engineer existing transformation logic, and improve the architecture for reliability, performance, and maintainability.
Evaluate and adopt ETL / integration tooling where it strengthens the pipeline, and reduce brittle or undocumented dependencies over time.
Serve as the data expert for the Commercial Analytics team — translate business needs into data requirements and explain data outputs in business terms.
Partner with the Director of Customer Data & Analytics and business stakeholders to ensure the data infrastructure supports commercial operations and decisions.
Coordinate with source-system owners and IT partners to secure reliable, well-understood data feeds.