Hybrid Senior Data Engineer, Analytics

Posted 2 weeks ago

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About the role

  • 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;
  • Pregnancy and responsible parenting course;
  • Partnerships with online course platforms;
  • Language learning platform;
  • And many more

Job title

Senior Data Engineer, Analytics

Job type

Experience level

Senior

Salary

Not specified

Degree requirement

Bachelor's Degree

Location requirements

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