Data Engineer

As a Data Engineer, you will be the backbone of our data strategy. You won't just be building pipelines; you will be designing the scalable infrastructure that powers our analytics, machine learning models, and business intelligence. We are looking for a mentor and a builder who thrives on optimizing complex data environments and ensuring 24/7 data reliability.

What you'll do:

• Architecture & Design: Design, build, and internalize large-scale batch and real-time data pipelines (ETL/ELT).
• Infrastructure Management: Optimize our data warehouse (e.g., Snowflake, BigQuery, Redshift) and lakehouse architectures for performance and cost.
• Data Governance: Implement rigorous data quality checks, monitoring, and alerting to ensure "one version of the truth."
• Tooling & Automation: Automate manual processes and improve developer productivity by implementing CI/CD for data workflows.
• Collaboration: Work closely with Data Scientists and Analysts to transform raw data into "analysis-ready" formats.
• Mentorship: Lead code reviews and provide technical guidance to junior members of the engineering team.

Your Expertise:

• Experience: 3-5+ years of experience in data engineering or backend software engineering.
• Coding: Mastery of Python or Scala/Java.
• SQL: Expert-level SQL (window functions, optimization, and complex joins are second nature to you).
• Big Data Tech: Experience with Spark, Flink, or Kafka.
• Orchestration: Hands on experience with Airflow, Dagster, or Prefect.
• Cloud: Proficiency in AWS, GCP, or Azure data ecosystems.
• Modeling: Deep understanding of data modeling techniques (Star Schema, Snowflake Schema, Data Vault 2.0).
• Experience withdbt (data build tool).
• Knowledge of Infrastructure as Code (Terraform).
• Background in managing containerized environments (Docker/Kubernetes).

Soft Skills

• Pragmatism: You know when to build a "quick and dirty" fix and when to invest in a robust, long-term solution.
• Problem Solving: You enjoy deconstructing a broken pipeline like a puzzle.
• Communication: You can explain the "why" behind a technical architecture to non-technical stakeholders.

Analytics CI/CD Extract Transform Load (ETL)