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2 Weeks ago

Data Engineer

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Bangalore, Karnataka, India
Information Technology
Full-Time
Acme Services

Overview

Job Description

  • Big Data Infrastructure & Pipelines:
  • Architect, develop, and optimize scalable data pipelines using Apache Spark, Hadoop, Apache Flink, and similar technologies for handling petabyte-scale datasets.
  • Build and maintain real-time data streaming pipelines using Apache Kafka, Apache Pulsar, or AWS Kinesis for processing low-latency, high-volume financial market data.
  • Implement batch data processing systems using Apache Spark and MapReduce for high-throughput, fault-tolerant data analytics.
  • Streaming Data Pipeline Experience - Real-time and Near realtime data processing experience.
  • Cloud Architecture:
  • Design and deploy cloud-native data solutions on AWS (S3, EMR, Lambda, Glue, Redshift), Google Cloud Platform (GCP) (BigQuery, Dataproc, Dataflow), or Microsoft Azure (Azure Data Lake, Synapse Analytics).
  • Use infrastructure-as-code tools such as Terraform or AWS CloudFormation to automate the provisioning and management of cloud resources.
  • Ensure cloud cost optimization and scalability for large-scale data processing tasks.
  • ETL Development:
  • Develop robust ETL (Extract, Transform, Load) pipelines using tools like Airflow, AWS Glue, or Dataflow, ensuring efficient ingestion and transformation of structured and unstructured data from multiple data sources.
  • Clean, transform, and enrich financial datasets (e.g., stock prices, options data, market feeds) for downstream analysis and machine learning model training.
  • Data Storage & Management:
  • Implement and manage data lakes and data warehouses (e.g., AWS Redshift, Google BigQuery, Snowflake) for storing large datasets with optimal querying performance.
  • Design database schemas and optimize performance for both OLAP and OLTP workloads.
  • Collaboration & Optimization:
  • Collaborate with data scientists, analysts, and other engineers to ensure the data platform supports advanced analytics, machine learning, and reporting requirements.
  • Continuously monitor data pipelines for performance bottlenecks and optimize for speed, scalability, and cost efficiency.
  • Data Governance & Security:
  • Implement data governance policies to ensure data quality, accuracy, and consistency.
  • Ensure data security and compliance with relevant regulations, especially when dealing with sensitive financial data.
  • Monitor and audit data access, implementing role-based access control and encryption standards.
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