Sr Data Engineer

• Implement Data Architecture: Implement scalable, secure, and efficient data architecture on on-prem and cloud platforms (Azure/GCP/AWS) to support business growth and data-driven decision-making.
• Collaborate with data engineers, and product teams to identify data requirements and develop data models that meet business needs.
• Data Ingestion and Integration:
• Develop and maintain data ingestion pipelines using various tools and technologies, such as Apache Spark, PySpark, Kafka, and Flume.
• Experience with GenAI tooling: LangChain, OpenAI API, Amazon Bedrock, or Vertex AI integrations, Claude, Copilot
• Integrate data from multiple sources, including relational databases, NoSQL databases, APIs, and files.
• Batch and Stream Processing:
• Develop and maintain batch and stream processing pipelines using tools like Apache Spark databricks.
• Integrate with messaging systems, such as Apache Kafka, Amazon Kinesis, and Google Cloud Pub/Sub.
• SQL Knowledge:
• Very strong SQL knowledge, including query optimization, indexing, and database design.
• Delta Lake and Data Warehouse:
• Design and implement Delta Lake and data warehouse/mart solutions to support business intelligence, reporting, and analytics.
• Develop and maintain data pipelines to ingest, process, and store data in Delta Lake and data warehouses.
• Distributed Databases and Data Warehousing:
• Implement and maintain data warehouses, such as Amazon Redshift, Google BigQuery, and Azure Synapse Analytics.
• Database Design and Development:
• Design, develop, and maintain efficient and scalable database systems across different platforms of relational databases (such as Oracle, MySQL, PostgreSQL, SQL Server).
• Collaborate with cross-functional teams to understand data requirements and translate them into effective database solutions.
• Implement and design data models and database schemas that align with business needs, ensuring data integrity and efficient data retrieval.
• Develop and optimize database queries, stored procedures, and functions for maximum performance and responsiveness.
• Performance Tuning and Optimization:
• Analyze and monitor database performance using diagnostic tools, identifying and resolving performance bottlenecks and inefficiencies.
• Optimize data processing workflows and queries to improve performance, reduce latency, and increase throughput.
• Data Management: Implement data archival mechanisms and data retention policies to ensure efficient data storage.
• Ensure the security and integrity of data by implementing access controls, data encryption, and backup and recovery strategies.
• Automation and Integration:
• Identify and implement automation solutions for data workflows.
• Collaborate with the development team to integrate database solutions into software applications effectively.
• Data Mart and Data Lake: Design and implement data marts and data lakes to support business intelligence, reporting, and analytics.
• Develop and maintain data pipelines to ingest, process, and store data in data lakes, such as Apache Hadoop, Amazon S3, and Azure Data Lake Storage.
• CI/CD and Automation:
• Develop and maintain automated testing, deployment, and monitoring scripts using tools like Jenkins, GitLab CI/CD, or similar.
• Ensure continuous integration and delivery of data pipelines and applications.
• Data Analysis and Modeling:
• Strong data analysis skills, including data modeling, data mining, and data visualization.
• Collaborate with data modelers to develop and implement data models to drive business insights and decision-making.
• Analyze complex data sets to identify trends, patterns, and correlations.
• Exploration of New Tools:
• Ability to explore new tools and technologies, and quickly develop proof-of-concepts (POCs) for data engineering open-source tools.
• Documentation: Document database design, configurations, and technical specifications.

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