StatusNeo - Data Engineer

Job Title: Data Engineer/ Sr. Data Engineer

\n

Experience Level: 3-6 Years

\n

Work Mode- Hybrid ( 2 PM to 11 PM )

\n

Location: Bangalore/Gurgaon

\n

Job Type: Full-Time

\n

Company- StatusNeo

About the Role We're looking for a Data Engineer who thrives on solving complex data challenges, building scalable pipelines, and delivering reliable insights that drive business decisions. The ideal candidate will bring hands-on expertise in cloud data platforms (AWS, GCP), Snowflake, Big Data ecosystems, and distributed computing using Spark and Scala. Key Responsibilities \n

\n
• \n

  Design, build, and maintain data pipelines for large-scale ingestion, transformation, and integration from multiple data sources.

\n
• \n

  Develop and optimize ETL/ELT workflows leveraging tools such as Spark , Scala , and SQL .

\n
• \n

  Implement data models and data warehousing solutions on Snowflake and cloud environments (AWS/GCP ).

\n
• \n

  Work closely with data scientists, analysts, and business stakeholders to deliver high-quality, production-ready data solutions.

\n
• \n

  Manage data quality , governance , and security across environments.

\n
• \n

  Optimize performance of data pipelines, focusing on scalability, efficiency, and cost optimization in cloud setups.

\n
• \n

  Collaborate with DevOps teams for CI/CD deployment and monitoring of data solutions.

\n
• \n

  Technical Skill Set (Must Have)

\n
• \n
  \n
  • \n

    Programming Languages: Scala, SQL

  \n
  • \n

    Big Data Frameworks: Apache Spark, Hadoop ecosystem

  \n
  • \n

    Cloud Platforms: AWS (S3, EMR, Glue, Redshift, Lambda) and/or GCP (BigQuery, Dataflow, Dataproc, Composer)

  \n
  • \n

    Data Warehouse: Snowflake (data modeling, performance tuning, query optimization)

  \n
  • \n

    ETL Tools: Spark-based ETL or Airflow/Glue/Composer

  \n
  • \n

    Version Control & CI/CD: Git, Jenkins, or equivalent

  \n
  • \n

    Data Formats: Parquet, Avro, ORC, JSON, CSV

    Good to Have \n
    \n
    • \n

      Knowledge of Python for data scripting and automation.

    \n
    • \n

      Familiarity with containerization (Docker, Kubernetes).

    \n
    • \n

      Experience with data cataloging and metadata management tools .

    \n
    • \n

      Exposure to streaming frameworks (Kafka, Pub/Sub).

    \n
  \n
\n