Design and implement scalable and reliable data pipelines to ingest, process, and store diverse data at scale, using technologies such as Apache Spark, Hadoop, and Kafka.
Work within cloud environments like AWS or Azure to leverage services including but not limited to EC2, RDS, S3, Lambda, and Azure Data Lake for efficient data handling and processing.
Develop and optimize data models and storage solutions (SQL, NoSQL, Data Lakes) to support operational and analytical applications, ensuring data quality and accessibility.
Utilize ETL tools and frameworks (e.g., Apache Airflow, Talend) to automate data workflows, ensuring efficient data integration and timely availability of data for analytics.
Collaborate closely with data scientists, providing the data infrastructure and tools needed for complex analytical models, leveraging Python or R for data processing scripts.
Ensure compliance with data governance and security policies, implementing best practices in data encryption, masking, and access controls within a cloud environment.
Monitor and troubleshoot data pipelines and databases for performance issues, applying tuning techniques to optimize data access and throughput.
Stay abreast of emerging technologies and methodologies in data engineering, advocating for and implementing improvements to the data ecosystem.
What We Need From You
Bachelor's Degree computer science, MIS, or other business discipline and 10+ years of experience in data engineering, with a proven track record in designing and operating large-scale data pipelines and architectures Req or
Master's Degree computer science, MIS, or other business discipline and 5+ years of experience in data engineering, with a proven track record in designing and operating large-scale data pipelines and architectures Req
Expertise in developing ETL/ELT workflows
Comprehensive knowledge of platforms and services like Databricks, Dataiku, and AWS native data offerings
Solid experience with big data technologies (Apache Spark, Hadoop, Kafka) and cloud services (AWS, Azure) related to data processing and storage
Strong experience in AWS and Azure cloud services, with hands-on experience in integrating cloud storage and compute services with Databricks
Proficient in SQL and programming languages relevant to data engineering (Python, Java, Scala)
Hands on RDBMS experience (data modeling, analysis, programming, stored procedures)
Familiarity with machine learning model deployment and management practices is a plus
Strong communication skills, capable of collaborating effectively across technical and non-technical teams
AWS Certified Solution Architect Preferred
Databricks Certified Associate Developer for Apache Spark Preferred