Data Engineer with Machine Learning
2 months ago
Technical Skills & Expertise:
- PySpark & Apache Spark:
- Extensive experience with PySpark and Spark for big data processing and transformation.
- Strong understanding of Spark architecture, optimization techniques, and performance tuning.
- Ability to work with Spark jobs in distributed computing environments like Databricks.
- Data Mining & Transformation:
- Hands-on experience in designing and implementing data mining workflows.
- Expertise in data transformation processes, including ETL (Extract, Transform, Load) pipelines.
- Experience in large-scale data ingestion, aggregation, and cleaning.
- Programming Languages:
- Python & Scala: Proficient in Python for data engineering tasks, including using libraries like Pandas and NumPy. Scala proficiency is preferred for Spark job development.
- Big Data Concepts: In-depth knowledge of big data frameworks and paradigms, such as distributed file systems, parallel computing, and data partitioning.
- Big Data Technologies:
- Cassandra & Hadoop: Experience with NoSQL databases like Cassandra and distributed storage systems like Hadoop.
- Data Warehousing Tools: Proficiency with Hive for data warehousing solutions and querying.
- ETL Tools: Experience with Beam architecture and other ETL tools for large-scale data workflows.
- Cloud Technologies (GCP):
- Expertise in Google Cloud Platform (GCP), including core services like Cloud Storage, BigQuery, and DataFlow.
- Experience with DataFlow jobs for batch and stream processing.
- Familiarity with managing workflows using Airflow for task scheduling and orchestration in GCP.
- Machine Learning & AI:
- GenAI Experience: Familiarity with Generative AI and its applications in ML pipelines.
- ML Model Development: Knowledge of basic ML model building using tools like Pandas, NumPy, and visualization with Matplotlib.
- ML Ops Pipeline: Experience in managing end-to-end ML Ops pipelines for deploying models in production, particularly LLM (Large Language Models) deployments.
- RAG Architecture: Understanding and experience in building pipelines using Retrieval-Augmented Generation (RAG) architecture to enhance model performance and output.
Tech stack : Spark, Pyspark, Python, Scala, GCP data flow, Data composer (Air flow), ETL, Databricks, Hadoop, Hive, GenAI, ML Modeling basic knowledge, ML Ops experience , LLM deployment, RAG
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