Lead II - Data Engineering

This role requires proficiency in developing data pipelines including coding and testing for ingesting wrangling transforming and joining data from various sources. The ideal candidate should be adept in ETL tools like Informatica Glue Databricks and DataProc with strong coding skills in Python PySpark and SQL. This position demands independence and proficiency across various data domains. Expertise in data warehousing solutions such as Snowflake BigQuery Lakehouse and Delta Lake is essential including the ability to calculate processing costs and address performance issues. A solid understanding of DevOps and infrastructure needs is also required.

Outcomes:

Measures of Outcomes:

Outputs Expected:

Code:

Documentation:

Configure:

Test:

Domain Relevance:

Manage Project:

Manage Defects:

Estimate:

Manage Knowledge:

Release:

Design:

Interface with Customer:

Manage Team:

Certifications:

Skill Examples:

Knowledge Examples:

Knowledge Examples

Job Summary As a Senior Data Engineer, you will be responsible for designing, building, and maintaining scalable data pipelines on an AWS cloud platform to support our data-driven initiatives. Your expertise in ETL data ingestion frameworks/tools will play a critical role in ensuring efficient data processing and integration. Accountabilities Create and maintain data ingestion pipelines, models, and architectures required to support a growing Data Marketing business Work with Product Management, business partners, and the Data Science team members to understand and create solutions to meet their needs Work with the Quality Engineers to validate solutions are meetings requirements. Implement automation processes as the opportunities present. Basic Qualifications: Familiarity with Data Pipeline Management Frameworks on Cloud (AWS Preferred, Azure, Google): As a Senior Data Engineer, you should have a strong understanding of data pipeline management frameworks offered by major cloud providers like AWS, Azure, and Google. Your expertise in working with these platforms will enable you to design and implement robust data pipelines to extract, transform, and load data from various sources. Familiarity with ETL Data Ingestion Framework/Tools. You should be well-versed in ETL (Extract, Transform, Load) data ingestion frameworks/tools, such as Azure Data Factory, Google Data Fusion, and SSIS. Your knowledge of these tools will facilitate seamless data integration and ensure data quality throughout the pipeline. Hands-on Experience with Python: Proficiency in Python is essential for this role. You should have hands-on experience using Python to develop data processing scripts, data manipulation, and transformation tasks, as well as implementing data engineering solutions. Knowledge of Source Control and Scrum Agile Software Development Methodologies: A strong foundation in source control practices, such as Bit Bucket, is required. Moreover, you should be familiar with Scrum Agile software development methodologies to effectively collaborate with cross-functional teams and deliver high-quality data engineering solutions. Familiarity with AWS Ecosystem: Having a deep understanding of the AWS ecosystem, including training jobs, processing jobs, and Sagemaker, will be a significant advantage. This knowledge will allow you to leverage AWS services efficiently and optimize data workflows. Preferred Qualifications: Experience in large-data solutions is highly desirable. Excellent verbal, written, and interpersonal communication skills. Experience with Scikit-learn, PyTorch, and Huggingface, and Building Transformer and Sentence Transformer Models: Your expertise in working with popular machine learning libraries like Scikit-learn, PyTorch, and Huggingface will be critical for developing and deploying transformer and sentence transformer models. Experience in building and fine-tuning these models will further enhance your role as a Senior Data Engineer.