Job Location: Beltsville, MD
Candidates Request Form 1 Job Title Data Analyst 2 Client Company/Dept. Name FSIS, OAS, ACQUISITIONS 3 Address MAILDROP 5230, 5601 SUNNYSIDE AVENUE City Name BELTSVILLE State Name MD-Maryland Zip Code 20705 If others (Address) 6 Duration of the project Project Start Date Oct 20, 2022 Project End Date Oct 19, 2023 Due date for Resume submission Sep 27, 2022 7 No. of Openings 1 No. of Maximum Submissions 2 8 Job Description 1.0 Scope of Work
The United States Department of Agriculture (USDA) has been protecting the nation’s food supply since 1906, with the inception of the Federal Meat Inspection Act. The Food Safety Inspection Service (FSIS) is the USDA agency responsible for food safety. The FSIS mission is to ensure that the nation’s commercial supply of meat, poultry, and egg products is safe, wholesome and correctly labeled and packaged as required by the Federal Meat Inspection Act, the Poultry Products Inspection Act, and the Egg Products Inspection Act. While carrying out this mission, FSIS generates both structured and unstructured data from its sampling, inspection, verification, and other activities.
This requirement is to provide assistance to the Office of Planning, Analysis, and Risk Management (OPARM) in addressing data quality issues found in Food Safety Inspection Service (FSIS) data systems and making recommendations to decrease data quality issues in the future. This includes utilizing test data systems, evaluating data tables for data quality issues, and developing SQL code to resolve data quality issues. Python, R, and SQL are the main tools used to complete this work, in addition to Microsoft Suite tools.
2.0 Background
OPARM coordinates FSIS’s data collection, analysis, and integration activities across all program areas. OPARM is responsible for ensuring data analyses are consistent and of high quality. OPARM conducts data analyses to inform mission and workforce-related Agency decisions, in addition to processing ad hoc and Freedom of Information Act data requests.
One of the key building blocks to data analysis is the data itself. While the Office of the Chief Information Officer is responsible for making actual changes and updates to the data, OPARM takes a leading role in finding data issues and making recommendations to clean the data and improve data quality. OPARM also implements preventive measures to minimize errors getting into the data by recommending enhancements to Agency systems, processes, and policies.
Contractor Requirements
3.0 Technical Requirements / Tasks
The Required Tasks Are As Follows
The contractor will be required to perform the tasks listed below. This list of tasks is not inclusive of ad-hoc and administrative tasks which the contractor may be required to complete.
Task 1: Identify Current Data Quality Issues
As a Part Of This Task The Contractor Shall
The contractor shall assist the Government in the identification of known and unknown data issues.
- Meet with the COR or the designated POC on a weekly basis to discuss and identify known data
issues, their status and resolution, and mitigations to the issue.
- Prepare a weekly status report detailing the previous week’s accomplishments and planned activities for the next week, along with targeted dates for each activity. The status report shall include summary information regarding the status of data quality tickets and counts of open vs. closed tickets.
- Monitor USDA’s customer relationship management/ticketing tool requests assigned to the Data Quality Queue and report any anomalies to the COR or COR’s designee.
- Reroute any tickets that belong to another queue to the appropriate POC.
- Monitor the Data Quality shared inbox and respond within two business days. Respond to ad hoc requests from OPARM or other program areas. Share ad hoc requests with the COR, who will determine the priority of requests and the appropriate method to respond. The contractor shall track responses for continuity of communication with the customers. Any ad hoc requests that do not come through USDA’s customer relationship management/ticketing tool shall be entered into this tracker by the contractor if it is determined that a data quality correction is required.
- Document all incoming data issues, regardless of the source, within a Microsoft Access database or similar system for internal use and reporting purposes.
- Maintain a record for each data quality issue, with each record containing as much detail as possible. The record shall include information including: Ticket number, requestor name and contact information, relevant status dates (i.e., when the issue was routed to the DQ queue, when it was submitted for data correction, and when it was resolved), categorization of the issue theme, subject area and problem classification. The contractor shall catalog these issues once identified and track relevant metrics and resolutions to these issues.
- The contractor will prepare a monthly catalog that shall be updated and distributed to the COR before the 5th business day of each month. It shall contain a summary page which identifies the activities for the reporting period, and additional pages to document types of issues that were identified, an analysis of tickets by subject area and issue type, the number of tickets in progress vs. resolved, in addition to any new system enhancement recommendations.
- The monthly catalog shall contain both text and data visualizations to represent the activities of the current reporting period and historical data.
- Assess each issue to determine both the likelihood of occurrence and impact on operations, analysis, and reporting. Likelihood and impact will be routinely reviewed and updated over time.
- Identify patterns based off known data issues to identify unknown data issues and add these to the catalog.
Task 2: Categorize and Identify the Cause of Data Issues
In Completion Of This Task The Contractor Shall
Use available resources to identify the source (root cause) of existing data issues.
- Use database tools, the Test FSIS data systems, available FSIS tools, and interviews with stakeholders to determine, when possible, the source of data issues.
- Develop a taxonomy of lifecycle data management and issue causes
- Causes
- Human error
ii. Data transfer issues
iii. Data entry tool problem
iv. Data changes
- System Error
vi. Other
- Once the taxonomy is approved by the COR, use it to categorize the data issues within the internal
tracking database and in monthly and weekly status reports.
- Update the Data Quality Catalog with these issues.
Task 3: Work with OCIO to Fix Current Data Problems
To Complete These Tasks The Contractor Shall
Use the Test FSIS data systems and test SQL database to develop SQL scripts to fix existing data quality issues. Develop a test plan, test the scripts, document the results, create a roll-back plan, and collect any additional information required by OCIO. Create a Request for Change (RFC) for each script (bringing about the correction of several data quality issues) and bring it through the RFC process. The contractor shall submit at least 2 RFCs per month.
- Write SQL scripts to fix data issues
- Develop a test plan
- Test the SQL script in the test database
- Test the roll-back plan
- Document the testing results
- Create a Request for Change (RFC) form and deployment plan document
- Bring the RFC through the FSIS RFC process
- Create and provide any additional information that is needed for the RFC process
- Work with OCIO to test the change in the pre-production environment
- Verify that the change was processed in the production system
- Close the ticket and notify the requestor that the data change is complete and the issue resolved.
Task 4: Identify Recommendations to Decrease Data Quality Issues in the Future
To Complete This Task The Contractor Shall
Use the sources of data quality issues identified in Task 3 to recommend changes to prevent similar data issues in the future.
- Identify which of the data issues in Task 3 could have been prevented by a system enhancement.
- For each of these issues, recommend methods to prevent them from reoccurring (or minimize their recurrence) in the future.
- Identify and measure how poor data quality impedes business objectives
- Define business-related data quality rules and performance targets
- Design quality improvement processes that remediate process flaws
- Implement quality improvements methods and processes
- Monitor data quality against targets
- Once recommendations are approved by the COR, submit a USDA’s customer relationship
management/ticketing tool request for each recommended fix, detailing the system issue, relevant examples, and a recommendation for resolution.
- Monitor these recommendations and ensure follow-up, including current status and dates of identification and resolution if they were resolved.
- All system enhancement recommendations shall be documented in the Microsoft Access database
(or other database system) and shall include the date submitted, issue type and issue information,
and the date of resolution. 9 Skill set info ” Have a bachelor’s degree in computer science, information science, or data analytics 10 Education ” Have a bachelor’s degree in computer science, information science, or data analytics 11 Certifications (if required) 12 Documentation Required for submission 13 Work Hours Monday Friday 0700-1630 14 Account Manager Name (Proposal Team) John Gabriel 15 Work authorization required US Citizen Only 16 Relocation is accepted Yes 17 Remote work Yes 18 Additional Notes if any
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