Job Summary

Key Responsibilities

Design, build, and maintain batch and/or streaming data pipelines to ingest, transform, and load data from various internal and external systems into data warehouses, data lakes, or lakehouses.
Develop and optimize data models, schemas, and storage layouts to support analytics, reporting, and machine learning use cases.
Implement data quality checks, monitoring, and alerting to ensure the accuracy, completeness, and timeliness of critical datasets.
Collaborate with data scientists, application developers, analysts, and business stakeholders to understand data requirements and deliver well-documented datasets and tables.
Manage and tune data infrastructure components, including databases, warehouses, orchestration tools, and distributed processing frameworks.
Apply security and governance best practices, including access controls, data lineage, and compliance with privacy regulations.
Contribute to engineering standards, documentation, and reusable tooling to improve the reliability and productivity of the data platform.

Required Qualifications

Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent practical experience).
Proficiency in at least one programming language commonly used in data engineering (e.g., Python, Java, or Scala).
Strong SQL skills and hands-on experience with relational databases and/or cloud data warehouses.
Experience building and operating ETL/ELT pipelines using modern data tools or orchestration frameworks.
Solid understanding of data modeling, data warehousing concepts, and performance optimization techniques.
Familiarity with at least one major cloud platform (e.g., AWS, Azure, GCP) and its data services.

Preferred Skills

Experience with big data or distributed processing technologies (e.g., Spark, Flink, Kafka, or similar).
Knowledge of BI/analytics tools and how engineers support dashboards and reporting.
Exposure to machine learning workflows and how data pipelines support model training and serving.
Experience working with an application development team and a software development life cycle (SDLC).
Use of modern AI tools and frameworks to increase productivity.