Data Engineer

2 days ago

tamil nadu, India SuperOps Full time

About the Role


We are looking for a Data Engineer to design, build, and maintain scalable data infrastructure that powers analytics, reporting, and AI-driven initiatives. You will work with modern columnar databases, design efficient ETL/ELT pipelines, and ensure the reliability and performance of our data ecosystem.


Responsibilities

  • Design, develop, and maintain data pipelines (batch and streaming) to ingest, process, and transform large-scale datasets.
  • Optimize query performance and storage efficiency in columnar databases (e.g., Snowflake, BigQuery, Redshift, ClickHouse).
  • Write efficient SQL for data modeling, transformation, and analytics use cases.
  • Collaborate with data analysts, scientists, and product teams to ensure data availability and reliability.
  • Implement data quality checks, monitoring, and alerting across pipelines.
  • Work with cloud platforms (AWS/GCP/Azure) and orchestration frameworks (Airflow, dbt, Dagster, etc.) to manage workflows.
  • Contribute to data architecture design including partitioning, indexing, and schema evolution strategies.
  • Ensure compliance with data governance, security, and privacy standards.

Requirements

  • Proven experience in data engineering (3–6 years preferred, but flexible based on role level).
  • Strong knowledge of SQL and experience with columnar databases (Snowflake, BigQuery, Redshift, DuckDB, ClickHouse, etc.).
  • Proficiency with ETL/ELT tools and workflow orchestration (Airflow, dbt, Luigi, Prefect, Dagster).
  • Experience with Python/Scala/Java for data engineering.
  • Familiarity with data lakes, lakehouses, and warehouse architectures.
  • Hands-on experience with cloud services (AWS S3/Glue, GCP Dataflow/BigQuery, Azure Data Factory, etc.).
  • Understanding of data modeling techniques (star schema, dimensional modeling).
  • Strong problem-solving and optimization skills for working with large datasets

Nice to Have

  • Knowledge of real-time streaming frameworks (Kafka, Flink, Spark Streaming).
  • Familiarity with DevOps practices (CI/CD for data pipelines, Docker, Kubernetes).
  • Exposure to machine learning pipelines or MLOps practices.
  • Experience with BI tools (Tableau, PowerBI, Looker).

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