Data Engineer

2 weeks ago

Bengaluru, Karnataka, India Brillminds Global Full time ₹ 15,00,000 - ₹ 25,00,000 per year

About the Role

We are seeking a highly skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate will have strong expertise in SQL, Python, and Apache Airflow, with experience in developing ETL workflows, optimizing data processes, and ensuring high data quality for analytics and business applications.

Key Responsibilities

  • Design, build, and manage ETL/ELT pipelines to process structured and unstructured data from multiple sources.
  • Develop, schedule, and monitor data workflows using Apache Airflow.
  • Write efficient and optimized SQL queries for data extraction, transformation, and loading.
  • Build reusable data components and automation frameworks using Python.
  • Ensure data quality, integrity, and governance across the data lifecycle.
  • Collaborate with Data Scientists, Analysts, and Product teams to deliver clean, reliable datasets.
  • Monitor and troubleshoot pipeline performance, ensuring high availability and scalability.
  • Implement data security, compliance, and best practices across systems.

Required Skills & Qualifications

  • Min 4 years of experience as a Data Engineer (or relevant role).

  • Strong expertise in:

  • SQL (complex queries, performance tuning, optimization)

  • Python (data processing, scripting, automation, libraries like Pandas, PySpark preferred)

  • Apache Airflow (DAGs, scheduling, orchestration)

  • Solid understanding of data warehousing concepts (e.g., star schema, fact/dimension modeling).

  • Experience with cloud platforms (AWS, GCP, or Azure) and cloud data warehouses (Snowflake, BigQuery, Redshift) is a plus.
  • Familiarity with version control (Git) and CI/CD for data workflows.
  • Strong problem-solving, debugging, and performance tuning skills.
  • Excellent communication and teamwork abilities.

Good to Have (Preferred but not Mandatory)

  • Experience with big data frameworks (Spark, Hadoop).
  • Knowledge of containerization (Docker, Kubernetes).
  • Exposure to data lakes and streaming technologies (Kafka, Flink).
  • Familiarity with ML pipelines and integration with AI/ML models.

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