Job Description

We are seeking a highly skilled Senior Big Data Engineer to design, develop, and manage enterprise-grade data integration solutions. The ideal candidate will have extensive experience with ETL/ELT processes, API-driven integrations, and enterprise data platforms. 

Key responsibilities

  • Architect, design, and optimize scalable big data solutions for batch and real-time processing.
  • Develop and maintain ETL/ELT pipelines to ingest, transform, and synchronize data from diverse sources.
  • Integrate data from cloud applications, on-prem systems, APIs, and streaming workspaces into centralized data repositories.
  • Implement and manage data lakes and data warehouses solutions on cloud infrastructure.
  • Ensure data consistency, quality, and compliance with governance and security standards.
  • Collaborate with data architects, data engineers, and business stakeholders to align integration solutions with organizational needs.

    • Core qualifications

  • Proficiency in Python, Java, or Scala for big data processing.
  • Big Data Frameworks: Strong expertise in Apache Spark, Hadoop, Hive, Flink, or Kafka.
  • Hands-on experience with data modeling, data lakes (Delta Lake, Iceberg, Hudi), and data warehouses (Snowflake, Redshift, BigQuery).
  • ETL/ELT Development: Expertise with tools like Informatica, Talend, SSIS, Apache NiFi, dbt, or custom Python-based frameworks.
  • APIs & Integration: Strong hands-on experience with REST, SOAP, GraphQL APIs, and integration platforms (MuleSoft, Dell Boomi, SnapLogic).
  • Data Pipelines: Proficiency in batch and real-time integration (Kafka, AWS Kinesis/ Azure Event Hub/ GCP Pub/Sub).
  • Databases: Deep knowledge of SQL (Oracle, PostgreSQL, SQL Server) and NoSQL (MongoDB, Cassandra, DynamoDB) systems.

    • Preferred experience

  • Expertise with at least one major cloud platform (AWS, Azure, GCP).
  • Experience with data services such as AWS EMR/Glue, GCP Dataflow/Dataproc, or Azure Data Factory.
  • Familiarity with containerization (Docker) and orchestration (Kubernetes).
  • Knowledge of CI/CD pipelines for data engineering.
  • Experience with OCI and Oracle Database (including JSON/REST, sharding) and/or Oracle microservices tooling.

    • How we’ll assess

  • Systems design interview: architect a scalable service; justify data models, caching, and failure handling.
  • Coding exercise: implement and optimize a core algorithm/data‑structure problem; discuss trade‑offs.
  • Code review: evaluate readability, testing, error handling, and security considerations.
  • Practical discussion: walk through a past end‑to‑end project, metrics/SLOs, incidents, and learnings.

    • Career Level - IC4

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