Senior Data Analyst

Description

Job Summary: Work as a Senior Data Analyst responsible for structuring and evolving the data ecosystem, operating end-to-end across the data lifecycle to support AI model development. Key Highlights: 1. Taking ownership in transforming data into actionable insights 2. Collaborative environment with challenges for learning and growth 3. Autonomy to propose and develop innovative projects **About Us** Since 2001, INDT has been a technology institute inspiring innovation and breathing technology! Development is in our DNA, and we offer a collaborative environment full of challenges for learning and growth. We originated in Manaus (AM) through Nokia and today operate nationwide, aiming to extend our services and culture across all regions of Brazil. Currently, we have over 200 active collaborators. Here, you’ll enjoy autonomy and ownership to propose and develop innovative projects. \#VEMSERINDT **About the Role** Work as a Senior Data Analyst responsible for structuring and evolving the data ecosystem across projects: modeling, ingestion, quality, governance, and analytics to support AI model development. In this role, you will take ownership in transforming data into actionable insights, operating end-to-end across the data lifecycle: defining metrics and sources, standardization, preparation, validation, publication, and consumption across analytical layers. We expect the ability to work autonomously, prioritize tasks based on impact, and collaborate with multidisciplinary teams to ensure consistency, reliability, and speed in decision-making. You will also contribute to best practices in documentation, governance, and continuous improvement, elevating the maturity of the data ecosystem. **Responsibilities:** * Define data models (conceptual/logical/physical). * Design and implement data ingestion and processing pipelines from diverse data sources. * Work with high-frequency and time-series data: timestamp standardization, synchronization, aggregations, and windowing. * Establish data quality rules (validations, reconciliation, deduplication, handling of missing values/outliers) and monitoring to prevent Garbage In / Garbage Out. Build analytical layers and KPIs for dashboards and to support process alerts and self-adjustments. * Conduct exploratory and statistical analysis to identify correlations and patterns across data. * Define success metrics and baselines, and design impact analyses (before/after, A/B when applicable). * Document data dictionaries, lineage, assumptions, and business rules; support the team with governance best practices. * Collaborate closely with Process Engineering/Fabril Engineering, Data Science/AI, and Software Engineering teams to translate business needs into data requirements. * Support data architecture reviews (scalability, performance, cost) and recommend technologies (ETL/ELT, caching, messaging, observability). Requirements: **Required Qualifications:** * Bachelor’s degree in Computer Science, Information Systems, Software/Computer Engineering, Systems Analysis and Development (Technologist), Database Management, Data Science, or related fields. * Solid experience with SQL (querying, modeling, performance, and optimization). * Experience with Python or R for data manipulation. * Knowledge of libraries and frameworks for data manipulation, transformation, and processing. * Analytical data modeling (dimensional/star schema) and concepts of data warehouse/lakehouse. * Building ETL/ELT pipelines and orchestration (e.g., Airflow, Prefect, Dagster, or equivalent). * Best practices in data quality, testing (e.g., Great Expectations or equivalent), and governance (catalog, dictionary, lineage). * Knowledge of relational databases (PostgreSQL, SQL Server, Oracle) and fundamentals of NoSQL. * Experience integrating via APIs (REST) and handling semi-structured data (JSON). * Familiarity with messaging/streaming and asynchronous processing (e.g., Kafka, RabbitMQ). * Cloud knowledge (AWS, Azure, or GCP), including data services and tools in datalake/lakehouse environments (storage, cataloging, querying, processing), and container usage (Docker) where applicable. * Experience or familiarity with cloud-based data analytics tools and datalakes (e.g., Athena/Glue/Redshift, BigQuery, Synapse, Databricks, Snowflake, or equivalents), including reading/writing columnar formats (Parquet/Delta) and partitioning best practices. **Desirable Technical Qualifications:** * Master’s degree in a related field (Data Science, Statistics, Computer Science, Engineering, or related disciplines). * Experience with industrial/IoT time-series data and protocols (OPC-UA, MQTT) or integration with automation systems. * Experience with dbt and Analytics Engineering practices. * Experience with distributed processing (Spark) and/or platforms (Databricks). * Knowledge of applied statistics, Statistical Process Control (SPC), and Root Cause Analysis (RCA) in manufacturing. * Familiarity with ML/feature engineering and collaboration with Data Science/MLOps teams. * Experience with pipeline observability (logs, metrics, tracing) and defining data SLAs/SLOs. **Language:** Advanced English (proficient writing, speaking, and comprehension of business and academic contexts, including meetings, documentation, and presentations). **Expected Soft Skills:** * Strong verbal and written communication skills, with the ability to explain analyses to both technical and non-technical audiences. * Autonomy in making technical decisions and managing priorities. * Experience working in agile environments (Scrum/Kanban) and daily collaboration with multidisciplinary teams. * Proactivity in identifying risks, proposing improvements, and aligning stakeholders. * Ownership mindset and focus on delivering value to the production process.