Analytics Engineer, Data Management
New
V
VeeamData management
Work model: full-remote from RomaniaFull-TimeMiddle
Salary not disclosed
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Job Details
- Languages
- Fluent English both spoken/written
- Experience
- 3+ years of experience in analytics engineering, data engineering, or BI engineering
- Required Skills
- PythonSQLPandasDatabricksPySpark
Requirements
- Have 3+ years of experience in analytics engineering, data engineering, or BI engineering.
- Use advanced SQL, including window functions, CTEs, complex joins, and query performance tuning.
- Use Python for data pipeline work, with PySpark or pandas.
- Understand dimensional modeling and slowly changing dimensions (SCD Type 2).
- Have experience building data processes on a cloud platform; Azure is preferred, and AWS or GCP are also considered.
- Have experience running production pipelines, including scheduling, monitoring, and troubleshooting failures.
- Have experience delivering datasets for recurring business reporting where accuracy and deadlines matter.
- Be able to explain technical trade-offs to non-technical stakeholders.
- Be fluent in spoken and written English.
- Have a bachelor’s degree in Computer Science, Information Systems, or a related field, or equivalent work experience.
Responsibilities
- Design, implement, and maintain scalable data marts and ETL/ELT processes using SQL or Python.
- Transform raw data from multiple sources into structured datasets for analysis and reporting.
- Collaborate with data analysts, data scientists, and business stakeholders to define data models.
- Build and orchestrate pipelines using Azure Data Factory and Databricks, including Python notebooks and Spark SQL.
- Deliver and maintain recurring Finance and Sales reporting datasets, including quarterly business reviews and sales compensation.
- Implement data quality checks and reconciliations so reported numbers can be traced to their source.
- Own pipeline scheduling, dependencies, monitoring, alerting, and root-cause analysis for failures or stale data.
- Work with Data, Architecture, and DevOps teams on deployments, infrastructure, and platform standards.
- Document data flows, models, and processes so teammates can support them.
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