Data Analyst – Mid-level

Jobtailor; São Paulo; Presencial; BRL 120 000 - 180 000; Tempo integral

Resumo da oferta

Jobtailor in São Paulo, Brazil, seeks a data-focused engineer/analyst to turn credit product data into strategic insights. You will build and optimize AWS-based data pipelines, write advanced SQL, and collaborate with product, tech and business teams to translate requirements into scalable analytics solutions.

The role emphasizes data quality, documentation, and modeling across raw, trusted and curated layers, with dashboards in QuickSight or BI tools to support decision making and regulatory

Qualificações

• 3–4 years in data analysis or analytical data engineering.
• Strong SQL and data modeling knowledge for data warehouses and data lakes.
• Hands-on AWS experience: S3, Glue, Step Functions, Athena.
• Experience with financial product data (cards, loans, financing) and related metrics.
• Proficient in Python and data automation using AWS integration (boto3).
• Experience with data visualization and dashboards in BI tools.
• Familiarity with data quality validations and documentation.

Responsabilidades

• Analyze credit product data to generate strategic business insights.
• Build and evolve analytical data pipelines in AWS (S3, Glue, Step Functions, Athena).
• Develop and optimize SQL queries for large data volumes.
• Ensure data quality and traceability through validations.
• Document transformations and metric definitions.
• Collaborate with product, tech and business teams to translate analytics needs.
• Participate in data architecture discussions and share knowledge with the team.

Conhecimentos

Advanced SQL

Python Pandas

Data Modeling

AWS

Data Visualization

BI Tools

Git GitHub

Jupyter Notebooks

QuickSight

Credit Data

Ferramentas

AWS S3

AWS Glue

AWS Step Functions

AWS Athena

Tableau

Power BI

Looker

GitHub

Jupyter

Descrição da oferta de emprego

Responsibilities

• Analyze credit product data (origination, usage, delinquency, risk, profitability) and generate strategic business insights.
• Build, maintain and evolve analytical data pipelines in an AWS environment (S3, Glue, Step Functions and Athena).
• Develop and optimize SQL queries for large volumes of data, supporting layered modeling in raw, trusted and curated stages.
• Ensure data quality, consistency and traceability by implementing validations and handling inconsistencies.
• Document processes, transformations and metric definitions.
• Work closely with product, technology and business teams, translating analytical requirements into scalable solutions.
• Participate in discussions on data architecture and modeling and share knowledge with the team.

Requirements

• Advanced SQL: CTEs, window functions (ranking, rolling aggregations, lag/lead), subqueries, complex joins, query optimization and execution plan analysis.
• AWS: hands‑on experience with S3 (storage and partitioning), Glue (ETL and cataloging), Step Functions (orchestration) and Athena (analytical queries).
• Data Modeling: dimensional modeling (star/snowflake), Data Warehouse, Data Lake, layered architecture (raw, trusted, curated) and partitioning.
• Data Quality: validation, handling inconsistencies, integrity/completeness testing and documentation of quality rules.
• Business Analysis: structuring product‑oriented analyses, defining and calculating metrics, interpreting results and clearly communicating findings.
• Minimum Experience: 3–4 years in data analysis or analytical data engineering.
• Python: Pandas, NumPy, automation of analytical processes and AWS integration via boto3.
• Data Visualization: creating dashboards and selecting appropriate visualizations for each analysis.
• Amazon QuickSight: building dashboards, calculated metrics and self‑service analyses.
• Financial Products: experience with credit product data (cards, loans, financing), financial metrics (delinquency, risk, profitability) and industry regulation.
• Complementary Tools: Git/GitHub, Jupyter Notebooks and BI tools (Tableau, Power BI, Looker).