Data Engineer

Description

Key Responsibilities:

Design, develop, and maintain scalable backend systems to support data warehousing and data lake initiatives.

Build and optimize ETL/ELT processes to extract, transform, and load data from various sources into centralized data repositories.

Develop and implement integration solutions for seamless data exchange between systems, applications, and platforms.

Collaborate with data architects, analysts, and other stakeholders to define and implement data models, schemas, and storage solutions.

Ensure data quality, consistency, and security by implementing best practices and monitoring frameworks.

Monitor and troubleshoot data pipelines and systems to ensure high availability and performance.

Stay up-to-date with emerging technologies and trends in data engineering and integration to recommend improvements and innovations.

Document technical designs, processes, and standards for the team and stakeholders.

Qualifications:

Bachelor's degree in Computer Science, Engineering, or a related field; equivalent experience considered.

Proven experience as a Data Engineer with 5 or more years of experience; or in a similar backend development role.

Strong proficiency in programming languages such as Python, Java, or Scala.

Hands-on experience with ETL/ELT tools and frameworks (e.g., Apache Airflow, Talend, Informatica, etc.).

Extensive knowledge of relational and non-relational databases (e.g., SQL, NoSQL, PostgreSQL, MongoDB).

Expertise in building and managing enterprise data warehouses (e.g., Snowflake, Amazon Redshift, Google BigQuery) and data lakes (e.g., AWS S3, Azure Data Lake).

Familiarity with cloud platforms (AWS, Azure, Google Cloud) and their data services.

Experience with API integrations and data exchange protocols (e.g., REST, SOAP, JSON, XML).

Solid understanding of data governance, security, and compliance standards.

Strong analytical and problem-solving skills with attention to detail.

Excellent communication and collaboration abilities.

Preferred Qualifications:

Certifications in cloud platforms (AWS Certified Data Analytics, Azure Data Engineer, etc.)

Experience with big data technologies (e.g., Apache Hadoop, Spark, Kafka).

Knowledge of data visualization tools (e.g., Tableau, Power BI) for supporting downstream analytics.

Familiarity with DevOps practices and tools (e.g., Docker, Kubernetes, Jenkins).

Requirements

Apache Spark, Python, Apache Hadoop, Apache Kafka, ETL - Extract Transform Load, Azure Data Factory, Azure Data Lake, Azure Databricks, Google Cloud Platform, Amazon Web Services (AWS), DevOps, Big Data, Data Warehousing

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