▷ 3 Days Left Data Engineer

4 weeks ago

Chennai, 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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