Data Analyst
About The Position
About Digacore Digacore is a fast-growing Managed Services Provider focused on operational excellence, automation, and data-driven decision-making. Our DNA (Data & Analytics) team is the backbone for reporting accuracy, analytics, and cross-department consistency. Role Summary As a Data Analyst on the DNA team, you will own data quality, analytics, and reporting across multiple business systems and teams—including Finance, Service, Projects, HR, and Account Management. You’ll transform multi-source data into clear insights and ensure consistency and trust in our reporting. Growth opportunity: collaborate with our DevOps/DNA team as we expand into AI-enabled reporting and chat-based analytics across systems like ConnectWise, IT Glue, and Microsoft Entra.
Requirements
- Strong SQL and ETL capabilities.
- Intermediate Power BI (or Tableau/Looker/equivalent).
- Advanced Excel (pivot tables, lookups, advanced formulas).
- Python or R familiarity for analysis and automation.
- Demonstrated ability to reconcile multi-source datasets and communicate findings clearly.
Nice To Haves
- Experience using AI/LLM tools to support analytics (insight generation, narrative summaries, chat-based Q&A).
- Familiarity with AI fundamentals like RAG/retrieval, embeddings, and evaluation/accuracy checks.
- Interest in integrating analytics into workflows (e.g., Teams-based insights, automation handoffs).
Responsibilities
- Data reconciliation: reconcile datasets across departments; identify and resolve discrepancies (billable hours, invoice totals, GL mapping, ticket/project inconsistencies).
- Dashboards & reporting: build, iterate, and maintain dashboards for Finance, Service, Projects, AM, and Leadership; deliver clear visualizations tailored to the audience.
- Data reliability: root-cause analysis when reports fail, refresh incorrectly, or produce conflicting results; implement fixes and preventive checks.
- Upstream collaboration: partner with DevOps and technical teams to improve data pipelines, refresh processes, and automation reliability.
- Standards & documentation: define and document data definitions, quality checks, and validation standards; support data governance and KPI consistency.
- Ad-hoc analytics: support stakeholders with ad-hoc data pulls, analysis, and insight generation.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
