Senior Data Engineer
Neshent Technologies
We are looking for an experienced Senior Data Engineer with strong expertise in Databricks, Python, PySpark, Apache Spark, and large-scale data engineering. The ideal candidate will be responsible for designing and developing scalable ETL pipelines, implementing data processing solutions, managing data ingestion frameworks, and optimizing cloud-based data platforms. The role also involves technical leadership, data governance, and collaboration with analytics and business intelligence teams.
Required Technical Skills
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Strong hands-on experience with Databricks, Delta Lake, Delta Live Tables, and Unity Catalog.
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Proficiency in Python, PySpark, Spark SQL, and Apache Spark.
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Strong experience in ETL development, data pipeline design, and data transformation.
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Knowledge of Hadoop, Kafka, Hive, HDFS, and Sqoop.
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Expertise in SQL, data modeling, and data warehousing.
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Experience with batch and real-time data processing using Kafka and Spark Streaming.
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Cloud experience with AWS and/or GCP.
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Strong understanding of data quality, validation, cleansing, and governance.
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Experience working in Agile environments (Scrum, Kanban, SAFe).
Preferred Skills
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Scala / Scala-Spark, IBM DataStage, TIBCO.
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AWS S3, Glue, Lambda, Kinesis/MSK.
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Tableau, Power BI, Spotfire, OAC.
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Teradata, DB2, BigQuery, Mainframe.
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Git, data governance, and retention modeling.
Roles & Responsibilities
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Design, develop, and maintain scalable data pipelines and ETL solutions.
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Build data ingestion and transformation frameworks for structured and unstructured data.
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Develop and optimize batch and streaming data processing solutions.
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Implement data quality, validation, and governance standards.
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Integrate heterogeneous data sources into enterprise data platforms.
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Support analytics, reporting, BI, and machine learning initiatives.
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Collaborate with Data Scientists, BI teams, and business stakeholders.
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Lead technical discussions, mentor junior engineers, and drive data engineering best practices.
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