At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.
With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.
We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.
About Us:
At CI&T, our rapid growth is fueled by the innovative solutions we create for our global clients. We are seeking a Data Analyst with a focus on data quality and reporting for a large-scale Finance data platform modernization at a major US mortgage lender — making sure the curated data and reports built on the new platform are correct and tie out to trusted Finance benchmarks. Pipeline-level monitoring and CI/CD are owned by the DataOps Engineer; this role is about the data and the numbers, not the pipeline mechanics.
Responsibilities:
• Define and run data quality checks and reconciliation logic against the curated data layer, comparing results to approved Finance benchmarks and closed accounting periods.
• Validate migrated and re-pointed reports and dashboards for parity against their prior legacy-source output, including parallel-run comparisons during cutover — running old and new side by side and confirming they tie out within agreed tolerance.
• Profile and investigate data quality issues and reconciliation variances, working with Data Engineering to identify and document root cause.
• Build and maintain data quality dashboards/reports in Power BI (or the equivalent BI tool) so quality status and known variances stay visible to Finance and delivery stakeholders on an ongoing basis.
• Partner with Finance and business stakeholders to define acceptance criteria and quality thresholds for each reporting deliverable.
• Produce the acceptance evidence — reconciliation results, parallel-run comparisons, sign-off packages — required at each delivery gate, including the cutover gate.
• Document data quality rules and known issues in a form the client's own team can maintain post-handoff.
Requirements:
• Solid experience as a data analyst or BI analyst, with strong hands-on SQL for querying, validating, and reconciling data.
• Hands-on experience building or validating reports/dashboards in Power BI or a comparable enterprise BI tool.
• Experience with a data-quality or reconciliation framework or methodology (e.g. dbt tests, Great Expectations, or a structured manual reconciliation practice).
• Experience with data in the financial/accounting domain, FP&A or Mortgage, including closed-period reconciliation constraints.
• Strong attention to detail and clear written documentation of data quality findings.
• Fluent English (B2) — coordinates quality findings and evidence directly with client stakeholders at delivery gates.
• Hands-on experience with Snowflake.
Nice to Have:
• Experience with financial/GL reporting domains (chart of accounts, dimensions, close process).
• Experience validating report or dashboard migrations off a legacy source system onto a new platform.
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