Senior Data Engineer
Some ghost-posting signals
- open for 387 days (90+ without a fill is a strong ghost signal)
- 46 open roles at this company in 30 days (mass-hiring blitz)
- no salary disclosed (correlates with ghost postings)
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About the role
Key Responsibilities
Design and implement general architecture for complex data systems.
Translate business requirements into functional and technical specifications.
Design and implement lakehouse architecture.
Develop and manage cloud-based data architecture and reporting solutions.
Apply data modelling principles for relational and dimensional data structures.
Design Data Warehouses following established principles (e.g.,Kimball, Inmon).
Create and manage source-to-target mappings for ETL/ELT processes.
Mentor junior engineers and contribute to architectural decisions and code reviews.
Minimum Qualifications
Bachelor’s degree in computer science, Computer Engineering, MIS, or related field.
5+years of experience with Microsoft SQL Server and strong proficiency in T- SQL, SQL performance tuning (Indexing, Structure, Query Optimization).
5+years of experience in Microsoft data platform development and implementation.
5+years of experience with PowerBI or other competitive technologies.
3+years of experience in consulting, with a focus on analytics and data solutions.
2+ years of experience with Databricks, including Unity Catalog, Databricks SQL, Workflows, and Delta Sharing.
Proficiency in Python and Apache Spark.
Develop and manage Databricks notebooks for data transformation, exploration, and model deployment.
Expertise in Microsoft Azure services, including Azure SQL, Azure Data Factory (ADF), Azure Data Warehouse (Synapse Analytics), Azure Data Lake, and Stream Analytics
Preferred Qualifications
Experience with Microsoft Fabric.
Familiarity with CI/CD pipelines and infrastructure-as-code tools like Terraform or Azure Resource Manager (ARM).
Knowledge of taxonomies, metadata management, and masterdata management.
Familiarity with data stewardship, ownership, and data quality management.
Expertise in Big Data technologies and tools:
o BigData Platforms: HDFS, MapReduce, Pig, Hive.
o General DBMS experience with Oracle, DB2, MySQL, etc.
o No SQL databases such as HBase, Cassandra, DataStax, MongoDB, CouchDB, etc.
o Experience with non-Microsoft reporting and BI tools, such as Qlik, Cognos, MicroStrategy, Tableau, etc.
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