Data Engineer focusing on data pipeline development and analytics for a global tech company. Collaborating with teams to ensure data availability and quality standards.
Responsibilities
Design, develop, and maintain data pipelines (batch and streaming) for ingestion, transformation, and delivery of data for analytics and application consumption.
Build and evolve analytical modeling (bronze/silver/gold layers, data marts, star schemas, wide tables), ensuring consistency, documentation, and reusability.
Implement data quality best practices (tests, validations, contracts, SLAs/SLOs, monitoring of freshness/completeness/accuracy) and manage incident resolution with root cause analysis (RCA).
Define and maintain technical data governance: catalog, lineage, versioning, naming conventions, ownership, access policies, and audit trails.
Optimize performance and cost of queries and pipelines (partitioning, clustering, incremental loads, materializations, job tuning).
Support the full delivery lifecycle (discovery → development → validation → operations), aligning business requirements with technical needs and ensuring predictability.
Collaborate with BI/Analytics teams to define metrics, dimensions, facts, and the semantic layer, ensuring traceability of key indicators.
Enable and operationalize AI/ML use cases.
Integrate sources and systems (APIs, databases, queues, events, files), ensuring security, idempotency, fault tolerance, and end-to-end traceability.
Produce and maintain technical and functional documentation relevant for auditing, support, and knowledge transfer.
Requirements
Proven experience as a Data Engineer focused on Analytics (building pipelines, modeling, and making data available for consumption).
Strong command of SQL and solid experience with Python (or an equivalent language) for data engineering and automation.
Experience with orchestration and workflow design (e.g., Airflow, Dagster, Prefect, or similar).
Experience with data warehouses/lakehouses and analytical formats/architectures (e.g., BigQuery, Snowflake, Databricks, Spark; Parquet, Delta, Iceberg).
Hands-on experience with ETL/ELT, incremental loads (CDC when applicable), partitioning, and performance/cost optimization.
Knowledge of data quality and reliability best practices (data testing, observability, metrics, incident management, and RCA).
Experience with version control (Git) and delivery practices (code review, branching patterns, basic CI).
Strong verbal and written communication skills for interacting with technical teams and stakeholders, with the ability to translate requirements into clear deliverables.
Benefits
Health and dental insurance;
Meal and grocery allowance;
Childcare assistance;
Extended parental leave;
Partnerships with gyms and health/wellness professionals via Wellhub (Gympass) TotalPass;
Profit-sharing program;
Life insurance;
Continuous learning platform (CI&T University);
Employee discount club;
Free online platform dedicated to physical and mental health and wellbeing;
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