Data Engineer-L3

• Serve as L3 support: triage high-severity incidents, perform advanced debugging/root-cause analysis, deploy hotfixes, and create runbooks for L2 teams.
• Build and maintain batch/streaming data pipelines using ETL/ELT tools (dbt,) to integrate and transform multi-source data.
• Implement data quality validation, monitoring, alerting, and documentation; optimize pipelines for performance, cost, and reliability (partitioning, indexing, error handling).
• Partner with analytics, data science, and business teams to deliver data requirements, troubleshoot issues, and ensure SLAs for freshness/completeness.

• 8-10+ years data engineering experience building and supporting production pipelines at scale.
• Design, build, and maintain data ingestion, transformation, and delivery pipelines across structured and semi-structured data sources.
• Develop modular, reusable data transformation logic using Python, SQL, and frameworks such as dbt.
• Implement data models and schemas optimized for analytics and reporting (star, snowflake, or dimensional).
• Apply Medallion Architecture principles to organize data layers for quality, traceability, and performance.
• Use cloud-native data services such as AWS Glue, S3, Redshift, EMR or Azure Data Factory, ADLS, Synapse to manage data workflows.
• Set up and manage data pipeline orchestration, scheduling, and monitoring using Airflow, ADF, or equivalent tools.
• Apply data quality checks, validation logic, and logging mechanisms to ensure consistency and trust in data assets.
• Collaborate with analysts, scientists, and architects to design data models that align with business and analytical needs.
• Maintain code versioning, testing, and CI/CD standards for data pipeline development.
• Proven cloud data platform + orchestration experience (Snowflake/BigQuery + Airflow/dbt).
• L3 support experience: incident management, on-call rotations, debugging distributed data systems.

Core Competencies & Skills

• Strong understanding of data engineering fundamentals - ETL/ELT design, data modeling, schema evolution, and data integrity.
• Proficient in Python and SQL for data transformation, automation, and workflow scripting.
• Hands-on experience with cloud-based data services in AWS (S3, Glue, Redshift, EMR) or Azure (ADLS, ADF, Synapse).
• Working knowledge of distributed data processing concepts (Spark, Hive, or equivalent).
• Familiarity with dbt for transformation design, testing, and data documentation.
• Awareness of Medallion Architecture and data layering concepts for scalable data management.
• Understanding of orchestration frameworks like Airflow or Data Factory for scheduling and monitoring pipelines.
• Knowledge of Git-based version control, CI/CD, and basic DevOps practices in data workflows.
• Have an AI skill set, a little bit on Claude, ChatGPT, and other tool supports, or at least who can pick up those skills.