Data Engineer

Job Summary:

Key Responsibilities:

• Data Pipeline Development:

  Design, develop, and maintain efficient and reliable ETL/ELT pipelines for ingesting, processing, and transforming large volumes of structured and unstructured data from diverse sources (e.g., databases, APIs, streaming sources, cloud storage).

• Data Architecture & Modeling:

  Design and maintain optimal data models, schemas, and database structures for relational (SQL) and NoSQL databases, data warehouses, and data lakes to support analytical and operational use cases.

• Big Data & Cloud Solutions:

  Work with big data technologies such as Apache Spark, Hadoop, Kafka, or Flink for large-scale data processing (batch and streaming).

• Collaboration & Support:

  Collaborate closely with data scientists to productionize machine learning models and ensure data availability for model training and inference.

• Data Governance & Security:

  Implement and enforce data security controls, access management policies, and best practices to protect sensitive information.

• Monitoring & Optimization:

  Monitor data pipeline performance, troubleshoot issues, and implement optimizations to enhance reliability, efficiency, and cost-effectiveness.

• Documentation:

  Document technical designs, data flows, workflows, and best practices to facilitate knowledge sharing and maintain comprehensive system documentation.

Required Qualifications:

• [8+] years of proven experience as a Data Engineer, Software Engineer with a data focus, or a similar role.
• Strong proficiency in at least one programming language commonly used in data engineering (e.g.,

  Python

  is highly preferred, Java, Scala).
• Expertise in

  SQL

  and strong experience with relational databases (e.g., PostgreSQL, MySQL, SQL Server) and database design.
• Hands-on experience with building and optimizing ETL/ELT pipelines.
• Familiarity with big data technologies (e.g., Apache Spark, Hadoop, Kafka).
• Experience with at least one major cloud platform (e.g.,

  AWS

  ,

  GCP

  , or

  Azure

  ) and its data services.
• Solid understanding of data warehousing concepts and data modeling techniques (dimensional modeling, Kimball, Inmon).
• Experience with workflow orchestration tools (e.g., Apache Airflow, dbt).
• Proficiency with version control systems (e.g., Git).