Torchora
Back to Home

Key Responsibilities and Required Skills for Input Analyst

💰 $ - $

Data EntryData ManagementBusiness OperationsAnalytics

🎯 Role Definition

The Input Analyst is responsible for accurate capture, validation, transformation and routing of structured and semi-structured data into operational systems and analytical environments. This role combines meticulous data-entry and quality-control with basic data engineering, business rules enforcement, and stakeholder communication to ensure that downstream teams — reporting, analytics, finance, operations, and compliance — receive consistent, reliable inputs. Strong attention to detail, working knowledge of data validation tools and SQL, and the ability to collaborate with business owners and IT make this role critical to data-driven operations.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Entry Clerk / Administrative Assistant with heavy data responsibilities
  • Junior Data Analyst or Reporting Assistant
  • Customer Operations Specialist or Transaction Processing Associate

Advancement To:

  • Senior Input Analyst / Data Quality Analyst
  • Data Steward or Data Governance Analyst
  • Business/Data Analyst or ETL Specialist

Lateral Moves:

  • Operations Analyst
  • Compliance or Audit Associate
  • CRM / ERP Functional Specialist

Core Responsibilities

Primary Functions

  • Accurately capture and enter high volumes of transactional and master data into enterprise systems (CRM, ERP, billing, claims platforms) following established formats, business rules, and SLAs to maintain data integrity across the organization.
  • Perform comprehensive data validation and reconciliation between source documents, scanned images/OCR output, and target systems; identify discrepancies and drive resolution with originators or subject-matter owners.
  • Design, document, and apply detailed data-entry procedures, mapping rules and transformation logic to ensure consistent handling of complex input scenarios and edge cases across multiple data channels.
  • Execute bulk data loads, incremental updates and patch operations using ETL scripts or provided import utilities; verify success and roll back changes when necessary to prevent data corruption.
  • Use SQL queries to extract, inspect and validate data sets; produce repeatable scripts to automate verification steps and support root-cause analysis when exceptions arise.
  • Configure and maintain automated data capture tools (OCR, document capture, RPA workflows) and validate their output; collaborate with automation engineers to tune recognition rules and minimize manual rework.
  • Maintain and enrich reference and master data (codes, pricing, product attributes) to support accurate downstream billing, reporting and analytics; raise change requests for system-of-record updates.
  • Monitor data quality KPIs (accuracy, completeness, timeliness, uniqueness) and produce daily/weekly exception reports; lead triage and remediation of priority defects to meet SLA commitments.
  • Investigate and resolve data exceptions and failed transactions; log incidents, coordinate corrective action with IT or business owners, and document fixes in the knowledge base.
  • Collaborate with Business Analysts, Data Engineers and Product Owners to translate ambiguous business requirements into clear data validation rules, sample cases and test scenarios.
  • Participate in design and execution of user acceptance testing (UAT) for new intake forms, API integrations and ingestion pipelines to validate mapping rules and business logic prior to production rollout.
  • Create and maintain data dictionaries, field-level metadata and source-to-target mapping documents to improve transparency and speed onboarding of new team members and business partners.
  • Apply data privacy and compliance controls during data processing, masking or archival; follow retention schedules, audit requirements and regulatory guidance to protect sensitive information.
  • Conduct periodic audits of data inputs and inventory control samples to quantify error rates, identify systemic issues and propose preventive controls to reduce manual corrections.
  • Prioritize backlogs of validation tasks and input requests using SLA-driven triage; communicate status proactively to stakeholders and escalate high-impact anomalies.
  • Implement and maintain basic automation (macros, Python scripts, RPA bots) to streamline repetitive validation tasks and reduce manual effort while maintaining an auditable trail.
  • Support cross-functional change management by documenting process flows, delivering training and providing day-one operational support for new data sources and intake channels.
  • Liaise with vendors and third-party data providers to verify delivery formats, acceptance criteria and error-handling policies; coordinate sample exchanges and onboarding tests.
  • Maintain robust version control and audit logs for manual corrections, data loads and configuration changes to ensure traceability and compliance for internal and external audits.
  • Produce operational dashboards and reports (Excel, Power BI, Tableau) summarizing throughput, error trends and SLA performance to inform management decisions and continuous improvement initiatives.
  • Assess and recommend improvements to intake forms, web capture fields and API payloads to reduce ambiguous inputs and increase automation coverage.
  • Provide level-2 support to front-line teams for complex input issues, act as escalation point for recurring errors and mentor junior staff on best practices and data hygiene.

Secondary Functions

  • Support ad-hoc data requests and exploratory data analysis.
  • Contribute to the organization's data strategy and roadmap.
  • Collaborate with business units to translate data needs into engineering requirements.
  • Participate in sprint planning and agile ceremonies within the data engineering team.
  • Prepare handover and operational runbooks for new integrations and third-party feeds.
  • Assist with periodic system and process reviews to support certification, audit and compliance initiatives.
  • Participate in cross-functional process improvement workshops (Lean / Six Sigma) to reduce cycle time and error rates.

Required Skills & Competencies

Hard Skills (Technical)

  • Proficient in SQL (SELECT, JOINs, GROUP BY, simple subqueries) for data extraction, validation and reconciliation tasks.
  • Advanced Excel skills (pivot tables, VLOOKUP/XLOOKUP, INDEX/MATCH, complex formulas and macros) for day-to-day validation and reporting.
  • Familiarity with at least one ETL or data integration tool (Informatica, Talend, Alteryx, Azure Data Factory) or experience using import utilities and load scripts.
  • Hands-on experience with document capture / OCR tools and post-processing (ABBYY, Kofax, UiPath Document Understanding) to validate extracted text and structured fields.
  • Experience with RPA platforms (UiPath, Automation Anywhere, Blue Prism) or scripting (Python, PowerShell) to automate repetitive ingestion and verification steps.
  • Practical knowledge of CRM/ERP systems (Salesforce, Microsoft Dynamics, SAP) or industry-specific transaction systems used as data targets.
  • Understanding of APIs, JSON/XML payloads and basic API testing tools (Postman) to support integration validations.
  • Data quality tooling or concepts experience (profiling, deduplication, validation rules) and ability to implement rule-based checks.
  • Familiar with BI and visualization tools (Power BI, Tableau) to create operational dashboards, root-cause visualizations and KPI tracking.
  • Version control basics (Git) and ability to maintain change logs and scripts in a repository for auditability.
  • Knowledge of data privacy, PII handling, GDPR/CCPA basics and secure data-handling practices.
  • Comfortable working with large spreadsheets and flat-file formats (CSV, fixed-width) including parsing and normalization.

Soft Skills

  • Extreme attention to detail and a methodical approach to repetitive tasks with a high accuracy requirement.
  • Strong analytical thinking and problem-solving skills; able to trace errors to root cause and recommend corrective actions.
  • Clear verbal and written communication with non-technical stakeholders; able to explain issues, risks and proposed fixes succinctly.
  • Time management and prioritization under tight SLAs and shifting operational priorities.
  • Team-oriented mindset with experience working in cross-functional and Agile environments.
  • Customer-service orientation when dealing with internal partners and external vendors.
  • Adaptability and willingness to learn new tools and processes rapidly.
  • Documentation discipline — ability to create and maintain runbooks, SOPs and knowledge articles.

Education & Experience

Educational Background

Minimum Education:

  • Associate degree, diploma or equivalent in a quantitative, business or technical field OR equivalent work experience in data-intensive operational roles.

Preferred Education:

  • Bachelor’s degree in Information Systems, Computer Science, Business Analytics, Finance, or related field.

Relevant Fields of Study:

  • Information Systems / Computer Science
  • Business Analytics / Data Science
  • Finance, Accounting, or Business Administration
  • Operations Management
  • Library & Information Science (for metadata and data stewardship focus)

Experience Requirements

Typical Experience Range: 1–5 years of experience in data entry, data validation, operations, or junior analytics roles.

Preferred: 2–4 years of direct experience working with intake systems, ETL/ingestion processes, SQL-based validation and automation tools; prior experience supporting cross-functional data onboarding projects is a plus.

(If you want, I can tailor this to a specific industry — finance, healthcare, insurance, or e-commerce — and add sample interview questions, a 30/60/90-day plan, or a short job posting optimized for job boards and search engines.)

Explore More Careers