Back
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Tier-1 brand, remote role, mid-level data engineer title, metro location and broad required skills increase competition.
Fundamental data engineering skills are transferable, but observability and incident-domain experience increase fit sensitivity to medium.
Explicit 5+ years plus mandatory Databricks/Spark/dbt/Python/SQL/DQ requirements create high shortlisting strictness.
Own end-to-end design, build, optimization, and maintenance of scalable batch and incremental data pipelines ingesting from Jira/JSM, OpsGenie, internal incident and telemetry sources.
Design and evolve data models and marts to produce trusted, performant datasets powering executive and team-level reliability analytics and reporting.
Implement automated data quality frameworks, anomaly detection, and alerting to ensure data pipeline reliability and prevent failures.
5+ years of data engineering experience building and operating production data platforms at scale.
Proficient in Python programming with software engineering fundamentals including OOP, testing, modular code, and CI/CD.
Expertise in SQL and data modeling skills including dimensional modeling, query tuning, and schema design.
Hands-on experience with Databricks/Apache Spark, dbt, and workflow schedulers (Airflow or Bitbucket Pipelines); familiarity with AWS or GCP data services.
Experienced operating in complex, high-scale data environments involving incident and reliability telemetry data.
Strong focus on data quality, observability, and automated monitoring in pipeline management.
Skilled in collaborating across engineering and leadership to translate ambiguous reliability requirements into technical solutions and documentation.