DescriptionStaples is business to business. You’re what binds us together.
Our digital solutions team is more than a traditional IT organization. We are a team of passionate, collaborative, agile, inventive, customer-centric, results-oriented problem solvers. We are intellectually curious, love advancements in technology and seek to adapt technologies to drive Staples forward. We anticipate the needs of our customers and business partners and deliver reliable, customer-centric technology services.
What you’ll be doing:
- Ensure that project/department milestones/goals are met and adhere to approved budgets.
- Define and deploy data architecture and enable top-down governance models.
- Oversee design and the creation of data architecture (models) and repositories.
- Develop data products from proof of concept to scalable solutions based on business priorities and impact, using modern tools and technologies on cloud.
- Champion technical design whiteboarding sessions to enable open discussion with architects, engineers & technologists.
- Enable data driven decision making: Advance access to information for decision making into wide populations of business users, by enabling them to access Data & Analytics via easy combinations of elements.
- Increase adoption of Data & Analytics (D&A) via self-service and data catalog.
- Manage the flow of data from internal and external sources by leveraging both distributed and local data structures.
- Optimize the data pipeline platform to support scalable workloads and diverse use cases.
- Support mission critical applications and near real time data needs in the data platform.
- Champion a good balance of federated data models vs. semantic data models owned by technology teams and understand the need for data silos owned by business partners.
- Participate in design and code reviews.
- Work with cybersecurity teams and infrastructure teams to secure and protect valuable data from inappropriate access or data loss.
- Work with disaster recovery planning and execution with other team members.
- Determine risk potential for various configurations and options for data administration and able to communicate issues to managers and team as required.
- Outcomes managed have an impact on the assigned section of work.
- Work hand in hand with product owners, scrum masters to deliver quality work and will be able to exercise independent judgment.
- Work collaboratively with project managers, technical data managers, data architects, data scientists as well as business partners.
- Work as part of an engineering team from concept to operations, providing deep technical subject matter expertise for successful deployment.
- Assist and develop internal standards for data engineering as well as system documentation.
- Accountable for the management of several segments in the enterprise data space.
- Allocate available resources to meet value stream & program objectives.
- Interact with direct reports and peers in management / customers / vendors to share information and improve cross-departmental processes.
- Typically manage individual contributors and junior managers of Data Solutions Engineering. Responsible for indirect reports as well.
- Ensure the ongoing training and development of direct and indirect reports.
What you bring to the table:
- Strong hands-on coding experience with languages like Python, PySpark, SQL, UNIX/Linux scripting to access, extract, manipulate and summarize data.
- Data Engineering: Experience in core data engineering activities as the following:
- Database optimization (partitioning, group and sort keys, indexes, query optimization),
- Data processing: (Spark, SQL Server, PostgreSql, Hadoop/Hive),
- Programming and/or scripting experience: (Python, PySpark, Bash),
- Data cleansing, Integration testing (PyTest or Unittest)
- Experience in automation and testing of data workflows, preferably Apache Airflow
- Familiarity with a broad base of analytical methods like Data modeling, variable based transformation & summarization, and algorithmic development.
What’s needed- Basic Qualifications:
- Bachelor’s Degree or equivalent in Computer Sciences, Data Analytics, Management Information Systems, or related quantitative field in Data Engineering
- 5+ years of experience in designing and building new scalable on-prem/cloud data engineering solutions using distributed frameworks like Spark, Hadoop etc.
What’s needed- Preferred Qualifications:
- Experience working with: Airflow, Kubernetes, Cloud infrastructure (e.g., Azure, GCP), CI/CD (e.g., Azure DevOps, Git actions, Jenkins, or CircleCI)
- Hands-on experience with cloud data warehousing services like Snowflake.
- Experience in SnowSQL, GCP - BigQuery, Cloud SQL, DataFlow, Pub/Sub, Cloud Functions etc.
- Experience in developing User Defined Functions (UDF) in python, SQL, etc.
- Experience with owning or overseeing ML Ops processes like Continuous Integration (CI), Continuous Delivery (CD), Continuous Training (CT), and Continuous Monitoring (CM) is a plus.
- Understanding of upcoming data technology trends like
- The Data Fabric from conceptualization to implementation
- Compostable Data & Analytics
- Enterprise journey to Decision Intelligence
- Experience in enabling a data marketplace solution, increasing reach of data products, and simplifying understanding and access for all consumers.
- Implementations of data API solutions to expand the reach of the data warehouse to multiple applications.
We Offer:
- Inclusive culture with associate-led Business Resource Groups
- Flexible PTO (22 days) and Holiday Schedule
- Online and Retail Discounts, Company Match 401(k), Physical and Mental Health Wellness programs, and more!
