We’re building a large-scale data analytics ecosystem powered by Microsoft Azure and Power BI. Our team integrates, transforms, and visualizes data from multiple sources to support critical business decisions. Data quality is one of our top priorities, and we’re looking for an engineer who can help us make our data landscape more reliable, transparent, and manageable.
Your responsibilities:
- Develop and maintain data quality monitoring frameworks within the Azure ecosystem (Data Factory, Data Lake, Databricks).
- Design and implement Data Quality Checks: validation, profiling, cleansing, and standardization.
- Detect data anomalies and design alerting systems (rules, thresholds, automation).
- Collaborate with Data Engineers, Analysts, and Business stakeholders to define data quality criteria and expectations.
- Ensure high data accuracy and integrity for Power BI reports and dashboards.
- Document data validation processes and recommend improvements to data sources.

Requirements:
- 3+ years of experience in a Data Quality, Data Engineering, or BI Engineering role.
- Hands-on experience with Microsoft Azure services (Data Factory, SQL Database, Data Lake).
- Advanced SQL skills (complex queries, optimization, data validation).
- Familiarity with Power BI or similar BI tools.
- Understanding of DWH principles and ETL/ELT pipelines.
- Experience with data quality frameworks and metrics (completeness, consistency, timeliness).
- Knowledge of Data Governance, Master Data Management, and Data Lineage concepts.

Would be a plus:
- Experience with Databricks or Apache Spark.
- DAX and Power Query (M) knowledge.
- Familiarity with DataOps or DevOps principles in a data environment.
- Experience in creating automated data quality dashboards in Power BI.

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