Back
Generalist Data Engineer title plus broad skillset and metro hiring increase candidate competition.
Data engineering skills are largely transferable across industries, so background sensitivity is low.
Explicit 1–3 year requirement and mandatory SQL/Python/warehouse skills create moderate shortlisting filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end design, development, and maintenance of production-grade ETL/ELT data pipelines ingesting multiple data sources into the central warehouse with focus on zero data loss and pipeline observability.
Design and maintain dimensional data models, partitioning strategies, and optimized warehouse architecture for analytical query performance and cost efficiency.
Develop reusable, tested Python and SQL code, manage CI/CD deployments, and build BI tools like dashboards and visualization frameworks for stakeholder data exploration.
Strong expertise in SQL for complex query optimization on cloud warehouses like BigQuery or Redshift.
Proficient in Python scripting for data ingestion and automation using libraries such as Pandas and SQLAlchemy.
Experience in end-to-end ETL/ELT pipeline development including incremental loads and backfills.
Bachelor's degree in a quantitative field and 1–3 years professional experience in data engineering or related backend analytics roles.
Hands-on engineer comfortable working across product, analytics, and backend teams to deliver reliable, maintainable, and high-quality data infrastructure.
Experienced with modern data warehouse architectures and trade-offs between normalized and denormalized models for analytical workloads.
Strong focus on data quality, pipeline observability, and automation with familiarity in tools like dbt, Airflow, Docker, and CI/CD pipelines.