
Data Engineer
20 hours ago
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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