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We are seeking a highly skilled Data Engineer to join our Infrastructure Strategy group at Reconocida empresa. As a Data Engineer, you will play a critical role in supporting the continued growth and scalability of our infrastructure organization.
Key Responsibilities- Partner with Leadership and Teams: Collaborate with leadership, engineers, program managers, and data scientists to understand data needs and develop strategic solutions.
- Design and Build Data Pipelines: Design, build, and launch efficient and reliable data pipelines to move data across multiple platforms, including data warehouses, online caches, and real-time systems.
- Communicate Data Insights: Communicate complex data insights to stakeholders through presentations, dashboards, company-wide datasets, and other mediums.
- Educate and Improve Processes: Educate partners on data and analytics best practices, identifying and addressing gaps in their existing logging and processes.
- Solve Large-Scale Problems: Leverage data and business principles to solve complex web, mobile, and data infrastructure problems.
- Own Data Quality: Build data expertise and own data quality for your areas, ensuring high-quality data is available for informed decision-making.
- 5+ years of Python development experience.
- 5+ years of SQL experience.
- 3+ years of experience with workflow management engines (i.e. Airflow, Luigi, Prefect, Dagster, Google Cloud Composer, AWS Step Functions, Azure Data Factory, UC4, Control-M).
- 3+ years experience with Data Modeling.
- Experience analyzing data to discover opportunities and address gaps.
- 5+ years experience in custom ETL design, implementation, and maintenance.
- Experience working with cloud or on-prem Big Data/MPP analytics platform (i.e. Netezza, Teradata, AWS Redshift, Google BigQuery, Azure Data Warehouse, or similar).
- Experience with more than one coding language.
- Experience with designing and implementing real-time pipelines.
- Experience with data quality and validation.
- Experience with SQL performance tuning and E2E process optimization.
- Experience with anomaly/outlier detection.
- Experience with notebook-based Data Science workflow.
- Experience with Airflow.
- Experience querying massive datasets using Spark, Presto, Hive, Impala, etc.
