Data Engineer
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Job Description
Data Engineer designing and maintaining data pipelines for analytics, AI, and machine learning platforms. Collaborating with teams to optimize data architectures in a data-driven environment.
Responsibilities:
- Design, build, and maintain robust ETL/ELT pipelines that move data from diverse source systems into analytics, AI, and machine learning platforms.
- Develop and optimize data architectures that support real-time and batch analytics, ML workflows, and personalization.
- Partner with data architects, analysts, and client stakeholders to translate business requirements into technical designs.
- Ensure data quality, lineage, observability, and security through validation, monitoring, and governance best practices.
- Optimize performance and cost-efficiency of pipelines and storage.
- Support cloud-first architectures across AWS, Azure, and GCP.
Requirements:
- 3–4+ years of experience in building production-grade data pipelines and data systems.
- Strong proficiency in SQL, Python, or Java/Scala.
- Experience with modern data platforms (Snowflake, BigQuery, Redshift, Databricks, Synapse).
- Hands-on skills with streaming technologies (Kafka, Kinesis, Pub/Sub).
- Knowledge of data modeling principles and schema design for analytics and operational systems.
- Familiarity with DevOps and CI/CD practices for data, including version control (GitHub/GitLab), testing, and deployment automation.
- Exposure to data governance, security, compliance, and observability frameworks.
- Ability to collaborate in a consulting/client-facing environment, balancing technical depth with pragmatic delivery.
Benefits:
- Generous paid company holidays, vacation, and paid sick time to every employee starting on day one.
- Top-of-the-line Health Insurance, plus monthly perks customized to meet individual employees’ needs.
- Support a healthy work/life balance.
- Remote work options available for certain roles.
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