Data Entry Specialist

Data Entry Specialist (DES) – Role Requirements and Service Description Role Overview The Data Entry Specialist (DES) owns the preparation and quality of the structured data behind pst.ag’s products. The role takes data published by authorities and other primary sources – PDFs, CSV files, XML feeds and website content – and cleans, maps and transforms it into correctly structured files that import without error into customer systems such as ERP and SAP and into our own platforms. The DES keeps that data current by monitoring the sources it comes from, proves its accuracy through documented quality checks, and continuously improves the process, increasingly with the help of AI and automation. This is an individual-contributor role. The DES is accountable for the accuracy, completeness and timeliness of the datasets it prepares, and for the traceability of every record back to its source. Key Responsibilities Data Preparation and Transformation • Prepare data provided by authorities and other sources (PDFs, CSVs, websites and similar) for import into customer systems (ERP, SAP and others) and pst.ag platforms, using spreadsheet programs (MS Excel, LibreOffice Calc), text editors and conversion to XML or CSV files that meet the target system’s specification. • Clean, convert and structure different data formats to meet technical requirements: character encoding (UTF-8, diacritics and non-Latin scripts), date, number and decimal formats, leading zeros and code formats, delimiters, and XML structure validated against the schema where one exists. • Preserve data integrity during every transformation. Codes, identifiers, names and dates are never silently altered, truncated or reformatted, and the original source file is always kept unchanged. Data Comparison, Mapping and Processing • Compare documents and successive versions of a source, extract the relevant information and process the findings in spreadsheets, producing a clear change set of what was added, amended or removed. • Match and reconcile lists between sources and systems; handle CSV and XML files safely and use complex spreadsheet formulas, lookups, pattern matching (regular expressions) and, where approved, scripts or queries for validation and transformation. • Detect and resolve duplicates, aliases, spelling variants and transliteration differences, and document the matching decision applied. Source Monitoring and Validation • Monitor official web pages, publications and feeds for updates, and identify new or changed data relevant to business requirements within the agreed turnaround, treating legally dated changes as time-critical. • Regularly check product data to ensure it is current, accurate and correctly mapped, and flag sources that change structure, move or stop publishing. • Maintain a source register: for each source, its URL or origin, update frequency, format, owner and known quirks. Quality Assurance and Compliance • Ensure structured data is consistently of high quality and fully compliant with legal and company quality requirements. • Carry out plausibility checks and thorough quality controls – record counts before and after, totals, mandatory fields, format and range checks, sample checks against the source – to detect and correct errors and inconsistencies before delivery. Process Control, Optimisation and Documentation • Control and continuously optimise the processes around data maintenance and updating, ensuring adherence to deadlines and efficiency standards. • Document results, project status and updates daily, using appropriate tools (spreadsheets, wiki, project tracking system). • Maintain versioned process documentation – source, method, transformation steps, checks performed and changes made – so that any record can be traced back to its source and any run can be repeated by a colleague. • Support internal audits and quality reviews related to data processes. Automation, AI and Innovation • Act as the key contact for IT regarding requirements and improvements for automated data-entry workflows; write clear requirements with sample input, expected output and test cases. • Identify repetitive manual tasks suitable for automation or AI assistance, and propose, test and document improvements – formulas, macros, scripts, data-cleaning tools or AI-assisted workflows. • Use AI tools within company rules to accelerate extraction, cleaning, mapping and documentation, and verify every AI-assisted result before it is used (see AI and Technology Literacy). • Proactively contribute innovative ideas, propose new processes and suggest workflow improvements for continual enhancement of data management services. Other Duties • Participate in meetings, trainings and other sessions for professional skill and process development. • Carry out additional related duties as directed. Requirements Essential Criteria • Several years of professional experience in data collection, data maintenance, data processing or a similar role, with confident handling of large data volumes. • Completed training or education in data processing, IT, business administration or a related field, or equivalent practical experience. • Hands-on expertise with spreadsheet and text-editing programs (MS Excel, LibreOffice Calc, Kate, Notepad++, Diff Checker or any data comparison and data conversion tools), including advanced formulas, lookups, pivot tables and filtering. • Strong skills in processing CSV and XML files, including encoding, delimiters and structure, with the ability to check a file against its specification. Desirable • Experience with customs tariffs, SPL (Sanctioned Party List) or similar regulatory data. • Knowledge of data cleaning tools such as OpenRefine, and of PDF-extraction and OCR tools. • Basic scripting or query skills (for example Python, SQL, regular expressions or Power Query) for repeatable transformations. • Understanding of ERP, SAP or similar enterprise systems and their import formats. • Experience using wiki systems for documentation, and working with ticketing or project tracking tools. • Proficiency in additional languages, particularly those of source data (for example German or Chinese). AI and Technology Literacy • Practical working knowledge of generative AI and large language models (for example ChatGPT, Claude, Copilot) and confident daily use of them for data work: extracting tables from PDFs and web pages, cleaning and normalising text, proposing field mappings, drafting spreadsheet formulas, regular expressions and scripts, and writing first-draft documentation. • Effective prompting and context design: the ability to break a data task into structured steps, supply sample data and the target format, and iterate to a usable and repeatable result. • Critical evaluation of AI output: recognises dropped or invented rows, silently changed numbers, dates, codes and names, lost leading zeros and diacritics, and inconsistent results between runs. Never uses AI output in customer data without verifying it against the source through record counts, reconciliation and sample checks. • Ability to define test cases and acceptance criteria for AI-assisted and automated workflows together with IT – sample input, expected output, edge cases – and to measure their accuracy before they are relied on. • Willingness to build and maintain internal AI knowledge assets: prompt libraries, curated sample files and documented AI-assisted workflows, shared with the team. • Openness to continuous learning as tools and company AI policy evolve, including adopting automation that changes how the role itself is performed.