Data Engineer

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 (36 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).