Data Engineering and Analytics Expert
5 days ago
About the Role:
We are seeking a seasoned Data Engineer and Analyst to join our team at Digihelic Solutions Private Limited. This role requires a strong blend of data engineering and analytics skills, with a focus on building and maintaining robust data pipelines and delivering actionable insights.
The ideal candidate will have extensive experience with cloud platforms (AWS/Azure), ETL processes, SQL, Databricks, PySpark/Apache Spark, and either NiFi or Hive.
Responsibilities:
- Data Pipeline Development & Management: Design, develop, and maintain scalable and efficient ETL/ELT pipelines using tools like Databricks, PySpark/Apache Spark, and either NiFi or Hive.
- Ingest, transform, and load data from diverse sources into cloud-based data warehouses and data lakes.
- Orient data pipelines for performance, reliability, and cost-effectiveness.
- Cloud Platform Expertise (AWS/Azure): Design and implement data solutions on either AWS or Azure cloud platforms.
- Utilize cloud-native services for data storage, processing, and analytics.
- Manage and optimize cloud resources for data engineering workloads.
- Data Warehousing & Data Lake Management: Design and implement data warehousing and data lake solutions for analytical purposes.
- Develop and maintain data models and schemas.
- Ensure data quality, consistency, and integrity.
- Programming & Scripting: Develop and maintain data processing scripts using Python and/or Scala.
- Utilize PySpark/Apache Spark for large-scale data processing and analysis.
- Implement data transformations and business logic using SQL.
- Data Analysis & Reporting: Analyze data to identify trends, patterns, and insights.
- Develop and maintain data visualizations and dashboards.
- Generate reports and presentations for stakeholders.
- Tool Proficiency (NiFi or Hive): Utilize either Apache NiFi for data flow automation and management or Apache Hive for data warehousing and SQL querying.
- Design and implement data flows or Hive queries for data processing and analysis.
- Collaboration & Communication: Collaborate with data scientists, analysts, and business stakeholders to understand data requirements.
- Communicate effectively with technical and non-technical audiences.
- Participate in agile development methodologies.
- Troubleshooting & Performance Optimization: Identify and resolve data pipeline and system issues.
- Orient data processing and query performance.
- Implement monitoring and alerting systems.
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