Senior Data Engineer

WHO WE NEED: 

We are seeking a highly skilled Senior Data Engineer with 5+ years of experience in data engineering to lead the design, development, and management of scalable data architectures and pipelines. The ideal candidate will have deep expertise in data warehousing, ETL processes, and cloud data platforms, along with a passion for optimizing data workflows and implementing best practices. As a Senior Data Engineer, you will work closely with cross-functional teams to ensure our data infrastructure supports analytics, reporting, and data-driven decision-making.

Qualifications:

• Extensive experience with cloud data platforms, primarily Azure (Data Lake, Synapse, Fabric), with additional experience in AWS (Redshift, S3, Glue) and Google Cloud (BigQuery, Dataflow) being a plus.
• Expertise in ETL tools and frameworks (e.g., Azure Data Factory).
• Proficiency in Python, Java, or Scala for data processing and automation tasks.
• Experience with data warehousing concepts and technologies (e.g., Delta lake and Synapse).
• Strong understanding of data modeling, data lakes, and distributed data systems (e.g., Hadoop, Spark).
• Strong proficiency in SQL and experience with relational databases (e.g., SQL Server, PostgreSQL, MySQL).
• Familiarity with version control tools (e.g., Git), CI/CD pipelines, and containerization (Docker, Kubernetes).
• Excellent problem-solving abilities and attention to detail.
• Strong communication and collaboration skills to work with cross-functional teams.
• Ability to manage multiple priorities in a fast-paced environment.

Preferred Skills:

• Experience with streaming data platforms (e.g., Kafka, Kinesis, Flink).
• Knowledge of machine learning pipelines and big data processing frameworks.
• Certifications in cloud technologies, such as Microsoft Certified: Azure Data Engineer Associate, AWS Certified Data Analytics, or Google Cloud Professional Data Engineer.

WHAT YOU’LL DO:

• Architect and Build Data Systems: Design, develop, and implement scalable and reliable data pipelines, data warehouses, and ETL solutions to manage and process large datasets efficiently.
• Data Integration: Collaborate with product, engineering, and data teams to ensure seamless data integration across multiple sources, ensuring data quality and integrity.
• Cloud Infrastructure: Leverage Azure to build, optimize, and scale data solutions.
• Data Modeling: Develop and maintain complex data models and schemas to support analytical and business needs.
• Data Governance: Ensure data security, privacy, and governance policies are in place and adhered to.
• Performance Tuning: Optimize query performance and data storage solutions to ensure timely and efficient data processing and retrieval.
• Mentorship: Provide technical leadership and mentoring to junior data engineers, fostering best practices and continuous learning.
• Collaboration: Work closely with stakeholders, product managers, analysts, and department leaders, to gather requirements and deliver data solutions that drive business insights.