Overview:
As a Data Engineer, you will play a critical role in transforming raw data into valuable insights that drive our business decisions. You will design, develop, and maintain data pipelines and infrastruc-ture across hybrid cloud environments, while also building robust reporting and visualization solu-tions.
- Responsibilities:
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- Data Pipeline Development: Design, build, and maintain scalable data pipelines to extract, transform, and load (ETL) data from various sources (e.g., databases, APIs, files) into data warehouses or data lakes.
- Hybrid Cloud Infrastructure: Manage and optimize data infrastructure across hybrid cloud environments, leveraging cloud-native services and on-premises resources.
- Data Quality: Ensure data quality through implementation of data validation, cleansing, and standardization processes.
- Reporting and Visualization: Develop interactive reports and dashboards using tools like Power BI, Tableau, or Looker to provide actionable insights to stakeholders.
- Data Governance: Adhere to data governance policies and procedures, including data se-curity, privacy, and compliance regulations.
- Data Modeling: Design and implement data models (e.g., dimensional, normalized) to op-timize data storage and retrieval.
- Automation: Automate data pipelines and processes using scripting languages (e.g., Py-thon, SQL) and automation tools.
- Collaboration: Work closely with data analysts, scientists, and business users to under-stand their requirements and deliver relevant data solutions.
Requirements:
- Experience: Proven experience as a Data Engineer or similar role with a focus on data pipelines, cloud infrastructure, and reporting.
- Technical Skills: Strong understanding of data engineering concepts, tools, and technolo-gies (e.g., SQL, Python, ETL tools, cloud platforms).
- Cloud Platforms: Experience with major cloud platforms (e.g., AWS, Azure, GCP) and their data-related services (e.g., data warehouses such as BigQuery/Redshift, data lakes, data pipelines).
- Data Modeling: Proficiency in data modeling techniques (e.g., dimensional, normalized) and data warehouse design.
- Reporting and Visualization: Expertise in using reporting and visualization tools (e.g., Looker, Power BI, Tableau) to create interactive dashboards.
- Problem-Solving: Ability to troubleshoot complex data-related issues and find innovative solutions.
- Communication: Excellent communication skills to collaborate effectively with cross-functional teams.
- Certifications: Preferred certifications (e.g., AWS Certified Data Engineer, Azure Certified Data Engineer, GCP Certified Professional Data Engineer).
Additional Skills (Preferred):
- Experience with data warehousing and data lake technologies (e.g., Google BigQuery, Amazon Redshift, Snowflake, Databricks)
- Knowledge of data analytics and machine learning concepts
- Familiarity with data governance and compliance frameworks (e.g., HIPAA, GDPR, CCPA)
