Key Responsibilities and Required Skills for Data Coordinator

🎯 Role Definition

We are seeking a proactive Data Coordinator to manage, validate, and streamline organizational data workflows. This role sits at the intersection of operations, analytics, and IT: you will be responsible for collecting and maintaining high-quality data, coordinating ETL and reporting processes, serving as the primary liaison between business stakeholders and technical teams, and ensuring compliance with data governance and privacy standards. The ideal candidate is detail-oriented, technically capable with SQL/Excel/BI tools, and experienced in translating business needs into usable data assets.

📈 Career Progression

Typical Career Path

Entry Point From:

Data Entry Specialist or Data Clerk transitioning into analytical responsibilities.
Administrative Assistant or Operations Coordinator with strong Excel/reporting experience.
Junior Data Analyst, reporting analyst, or ETL support technician.

Advancement To:

Senior Data Coordinator or Lead Data Steward.
Data Analyst or Business Intelligence Analyst.
Data Governance Analyst or Data Operations Manager.
Data Engineer (with stronger technical upskilling) or Analytics Manager.

Lateral Moves:

Business Systems Analyst
CRM Administrator
Project Coordinator (data-focused)

Core Responsibilities

Primary Functions

Coordinate, collect, validate, and centralize datasets from multiple internal and external sources, ensuring completeness and consistency across CRM, ERP, marketing, and finance systems.
Design, document, and maintain ETL routines and data ingestion processes—using SQL, Python, or ETL tools—to reliably extract, transform, and load data into the organization’s data warehouse or reporting layer.
Build, optimize, and maintain SQL queries, views, and stored procedures to support recurring reports, ad-hoc analysis, and dashboard feeds while ensuring performance and scalability.
Execute rigorous data quality checks and reconciliations on incoming and existing datasets, identify root causes of anomalies, correct data issues, and implement preventative measures to reduce rework.
Maintain and publish authoritative data dictionaries, metadata, lineage documentation, and standard operating procedures (SOPs) so analysts and business users can understand and reuse data correctly.
Act as the primary point of contact for internal stakeholders requesting data extracts, dashboards, and analytical support; translate business requirements into technical specifications for data engineering and analytics teams.
Monitor data pipelines, scheduled jobs, and integrations; quickly triage failures, coordinate with engineering or vendor support, and communicate incident status and resolution timelines to stakeholders.
Create, update, and automate operational and executive reports using Excel, Google Sheets, Power BI, or Tableau, including KPI tracking, trend analysis, and variance explanations.
Maintain master data hygiene for customers, products, vendors, and other reference data—perform deduplication, standardization, enrichment, and version control activities.
Support implementation and enforcement of data governance practices including access controls, data stewardship roles, data classification, and lifecycle management in collaboration with governance teams.
Manage and prioritize incoming data requests using ticketing tools (e.g., Jira, ServiceNow), maintain a backlog, and ensure transparent SLAs and turnaround times for business units.
Facilitate cross-functional data projects—coordinate timelines, resources, testing plans, UAT, and rollout communications to ensure successful integrations and migrations.
Train and support end users on reporting tools, data definitions, and request processes; prepare user guides and conduct training sessions to increase data literacy.
Prepare and present recurring performance summaries and insight briefs to product, marketing, sales, and finance leaders; recommend data-driven actions and improvements.
Implement and maintain data security and privacy controls for sensitive datasets, support audit activities, and ensure compliance with regulations such as GDPR, CCPA, or sector-specific requirements.
Assist data architects with data modeling efforts, maintain logical and physical schema documentation, and help evaluate the impact of schema changes on downstream consumers.
Participate in vendor evaluations and onboarding for ETL, BI, and data catalog solutions; provide operational requirements and support proof-of-concept activities.
Conduct periodic archival, cleanup, and retention activities to maintain optimal storage performance and comply with retention policies.
Support data migration efforts during system upgrades or replacements by mapping source-to-target fields, performing test loads, and validating reconciliation results.
Create and maintain automated quality assurance scripts and monitoring dashboards to proactively surface data drift, missing feeds, or schema changes.
Standardize and streamline manual data processes into repeatable, automated workflows that reduce manual touchpoints and improve reliability.
Track and report on dataset inventory, usage metrics, and cost drivers for cloud storage and processing to inform budgeting and optimization efforts.
Collaborate with legal and compliance teams to support data requests tied to audits or litigation holds and to document chain-of-custody for critical datasets.

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.
Represent the data team in cross-functional working groups and governance councils to align data priorities with business goals.
Assist with vendor contract renewals and licensing management for reporting and ETL tools.
Help develop onboarding checklists and templates for new datasets and third-party integrations.

Required Skills & Competencies

Hard Skills (Technical)

Strong SQL skills: writing complex joins, window functions, CTEs, index-aware query optimization, and performance tuning.
Advanced Excel skills including pivot tables, Power Query, VLOOKUP/XLOOKUP, macros, and complex formula construction.
Experience with ETL/integration tools or frameworks (e.g., Talend, Informatica, Fivetran, Stitch) and orchestration/scheduling tools (Airflow, Cron, Control-M).
Familiarity with BI and dashboarding tools such as Power BI, Tableau, Looker, or Google Data Studio; ability to design clear, actionable dashboards.
Knowledge of data warehousing concepts and platforms (Snowflake, Redshift, BigQuery, Azure Synapse).
Working knowledge of a scripting language for automation and transformation (Python or R) and libraries for data processing (pandas, dplyr).
Experience with CRM/ERP systems (Salesforce, NetSuite, SAP) and managing integrations between these systems and analytics platforms.
Understanding of data governance, metadata management, data catalogs (Alation, Collibra), and master data management principles.
Familiarity with data security, privacy frameworks, and regulatory requirements (GDPR, CCPA, HIPAA where applicable).
Experience with API integrations, JSON/XML data formats, and basic web/data service troubleshooting.
Version control basics (Git) and documentation tooling (Confluence, SharePoint, Notion).
Experience using ticketing and project management tools (Jira, Asana, ServiceNow).
Basic knowledge of statistical quality checks, anomaly detection, and KPI definition.

Soft Skills

Strong communication skills: translate technical details into business-friendly language and present findings to stakeholders.
Excellent attention to detail and quality orientation when validating and reconciling datasets.
Proven stakeholder management and ability to manage expectations across cross-functional teams.
Effective time management, prioritization, and ability to handle competing deadlines in a fast-paced environment.
Problem-solving mindset with ability to diagnose root causes and implement sustainable fixes.
Collaborative team player with the ability to lead data-related discussions and coordinate cross-functional effort.
Adaptability and willingness to learn new tools, processes, and domain knowledge rapidly.
Documentation-driven approach: create clear runbooks, SOPs, and onboarding guides.
Customer-service oriented with a focus on timely delivery and user satisfaction.
Project coordination and basic project management skills to track deliverables and dependencies.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree in a related field such as Information Systems, Data Science, Computer Science, Statistics, Business Administration, or Economics; or equivalent professional experience.

Preferred Education:

Bachelor’s or Master’s degree in Data Science, Computer Science, Information Systems, Business Analytics, or a closely related discipline.
Relevant certifications (e.g., Microsoft Power BI, Tableau Desktop, Snowflake/BigQuery fundamentals, SQL certifications) are a plus.

Relevant Fields of Study:

Data Science / Analytics
Computer Science / Information Technology
Business Analytics / Economics
Statistics / Mathematics
Information Systems / Management Information Systems

Experience Requirements

Typical Experience Range: 2–5 years in data operations, reporting, ETL support, or a related role.

Preferred:

3+ years of experience coordinating data workflows, managing reporting pipelines, or working in a data stewardship role; experience in regulated industries or large-scale SaaS/cloud environments is a strong plus.