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Key Responsibilities and Required Skills for Input Consultant

💰 $ - $

ConsultingData OperationsBusiness Systems

🎯 Role Definition

The Input Consultant is an operational and strategic specialist who designs, implements and governs the processes, controls and system configurations that enable accurate data input across business systems (ERP, CRM, financial systems, reporting platforms). Working closely with business stakeholders, system owners, data engineers and analytics teams, the Input Consultant defines input standards and validation rules, performs root-cause data investigations, leads data migration and UAT activities, and implements automation or process improvements to reduce manual effort and error rates. This role is ideal for professionals with strong data accuracy mindset, business-process knowledge and experience with enterprise systems and data validation tools.

Key keywords: Input Consultant, data input, data quality, data validation, ERP input, CRM data entry, data governance, process improvement, UAT, automation, RPA, SQL, Excel.

📈 Career Progression

Typical Career Path

Entry Point From:

  • Data Entry Specialist or Data Analyst with strong process orientation.
  • Business Analyst or Functional Consultant (ERP/CRM) responsible for master data.
  • Operations Analyst supporting transaction processing and reconciliations.

Advancement To:

  • Senior Input Consultant / Data Governance Lead
  • Business Systems Manager or ERP/CRM Functional Lead
  • Data Quality Manager or Master Data Management (MDM) Lead

Lateral Moves:

  • Business Analyst (process & systems)
  • Data Engineer (with additional technical upskilling)

Core Responsibilities

Primary Functions

  • Design, document and implement end-to-end data input workflows and standard operating procedures (SOPs) for ERP, CRM and reporting systems to ensure consistent, repeatable capture of transactional and master data across business units.
  • Lead data validation and reconciliation activities for complex data sets during routine operations and at month-end/quarter-end closes to ensure ledger, reporting and operational systems are aligned and accurate.
  • Define, configure and maintain data validation rules, mandatory field checks, format constraints and automated alerts in source systems or middleware to prevent inaccurate data from entering downstream processes.
  • Execute and manage large-scale data migration and cleansing projects, including mapping source-to-target fields, creating transformation scripts, executing test loads and logging/triaging migration issues until resolution.
  • Develop and maintain detailed data quality metrics and dashboards (e.g., error rates by data type, timeliness, completeness) and present insights and remediation plans to stakeholders and leadership.
  • Perform root-cause analysis for recurring input errors, identify process and system improvements, and lead cross-functional remediation efforts to permanently reduce rework and write-offs.
  • Author and update user guides, data entry training materials and step-by-step playbooks for frontline teams to improve first-pass accuracy and reduce exceptions.
  • Coordinate and lead User Acceptance Testing (UAT) and system integration testing for enhancements that affect data capture, validation, or mapping; document test plans, test cases and defect logs.
  • Act as the subject-matter expert for master data governance, including ownership of data definitions, allowable values, lifecycle rules and approval workflows to support consistent business decisions.
  • Implement automation and scripting (RPA, macros, ETL jobs) to replace repetitive manual data entry steps, accelerate throughput and lower human error exposure.
  • Configure and manage data interfaces and API-based integrations between transactional systems and reporting/analytics platforms to ensure timely and accurate data flows.
  • Work directly with internal and external stakeholders (finance, sales operations, supply chain, IT vendors) to translate business requirements into system configurations and data validation rules.
  • Monitor data inbound/outbound queues and exception logs daily, triage priority incidents and coordinate fixes with technical teams to minimize operational impact.
  • Conduct periodic data audits and sampling exercises to validate adherence to input standards and to identify training or governance gaps requiring targeted interventions.
  • Provide hands-on support and escalation coverage for complex or high-risk data entry activities (e.g., journal adjustments, contract setup, pricing changes) and validate outcomes before promotion to production.
  • Translate business policies into executable data policies and change control procedures that preserve data integrity while enabling controlled process change.
  • Establish KPIs and SLAs for data capture activities and hold operational teams accountable through regular performance reviews, coaching and remediation plans.
  • Collaborate with analytics and BI teams to ensure source data quality and lineage are documented, enabling reliable reporting and model training data.
  • Drive continuous improvement initiatives (Lean, Six Sigma principles) to reduce touchpoints and cycle time in data capture processes and to eliminate non-value-added tasks.
  • Supervise or mentor junior data input specialists and analysts, perform regular quality reviews and deliver targeted coaching sessions to raise team competency.
  • Manage vendor relationships and third-party data providers to ensure incoming data meets contractual quality thresholds and is delivered on schedule.
  • Maintain a prioritized backlog of system fixes, validation enhancements and training needs, and work with product/IT owners to schedule incremental deliveries.

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.

Required Skills & Competencies

Hard Skills (Technical)

  • Master Data Management (MDM) and Data Governance: experience defining data domains, ownership, validation rules and governance processes to maintain consistent master records across systems.
  • ERP/CRM Configuration: hands-on configuration experience in at least one major ERP or CRM platform (e.g., SAP ECC/S4HANA, Oracle E-Business Suite, NetSuite, Microsoft Dynamics, Salesforce).
  • Data Validation & Reconciliation: proven ability to design and execute reconciliation processes, write validation queries and automate exception detection.
  • SQL & Querying: strong SQL skills for data profiling, ad-hoc validation, and building validation scripts or stored procedures.
  • Excel Advanced: expert-level Excel skills including VLOOKUP/XLOOKUP, INDEX/MATCH, pivot tables, conditional formatting and macros/VBA for data transformation and reconciliation tasks.
  • ETL and Data Integration: familiarity with ETL tools or middleware (e.g., Informatica, Talend, SSIS, MuleSoft) and experience mapping and troubleshooting data flows.
  • Scripting & Automation: practical experience building automations using Python, RPA tools (UiPath, Automation Anywhere, Blue Prism) or macros to eliminate manual entry work.
  • Testing & UAT: experience designing test plans, test cases and performing UAT for data-centric system changes; ability to validate results and manage defect lifecycle.
  • Reporting & BI Tools: working knowledge of BI platforms (Power BI, Tableau, Qlik) to monitor data quality KPIs and present actionable insights.
  • Data Quality Tools: experience using data quality and profiling tools or modules, and implementing rules to measure completeness, uniqueness and validity.
  • APIs & System Integrations: knowledge of REST/SOAP APIs and how to validate payloads and data mapping for real-time or batch integrations.
  • Regulatory & Compliance Awareness: familiarity with regulatory data requirements relevant to finance, privacy (GDPR), or industry-specific reporting standards.

Soft Skills

  • Attention to Detail: meticulous approach to catching inconsistencies and preventing data defects that impact downstream processes and reporting.
  • Stakeholder Management: ability to build credibility with cross-functional teams and lead change with both technical and non-technical stakeholders.
  • Analytical Thinking: structured problem-solving skills and comfort working with ambiguous data problems to identify root causes and solutions.
  • Communication & Documentation: strong written and verbal communication to produce clear SOPs, training materials and executive-level status updates.
  • Project Management: ability to prioritize, manage deliverables and coordinate multiple stakeholders to meet deadlines in a fast-paced environment.
  • Continuous Improvement Mindset: proactive, metrics-driven approach to reduce error rates and streamline data capture operations.
  • Coaching & Leadership: experience mentoring junior staff and delivering targeted training to improve team performance.
  • Adaptability: flexibility to shift between tactical incident response and strategic process design tasks.
  • Time Management: capability to manage high-volume workloads and respond to urgent escalations without sacrificing accuracy.
  • Customer Service Orientation: focus on internal customer needs, responsiveness to queries and commitment to meeting SLAs.

Education & Experience

Educational Background

Minimum Education:

  • Bachelor's degree in Business Administration, Information Systems, Computer Science, Accounting, Finance, or related field (or equivalent practical experience).

Preferred Education:

  • Bachelor’s or Master’s degree in Information Systems, Data Management, Business Analytics, or a related discipline.
  • Certification in data governance, Lean/Six Sigma, or relevant ERP/CRM platforms (e.g., Salesforce Admin, SAP FI/CO).

Relevant Fields of Study:

  • Information Systems / Computer Science
  • Business Administration / Finance / Accounting
  • Data Analytics / Business Intelligence
  • Operations Management

Experience Requirements

Typical Experience Range:

  • 3–7 years of progressive experience in data input operations, business systems, ERP/CRM administration, or data quality roles.

Preferred:

  • 5+ years working with enterprise systems (ERP/CRM), leading data migration or data governance initiatives, and demonstrable experience implementing automation or validation frameworks that reduced error rates and improved operational efficiency.
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