Role Proficiency:

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:

      Act creatively to develop pipelines/applications by selecting appropriate technical options optimizing application development maintenance and performance through design patterns and reusing proven solutions. Support the Project Manager in day-to-day project execution and account for the developmental activities of others.       Interpret requirements create optimal architecture and design solutions in accordance with specifications.       Document and communicate milestones/stages for end-to-end delivery.       Code using best standards debug and test solutions to ensure best-in-class quality.       Tune performance of code and align it with the appropriate infrastructure understanding cost implications of licenses and infrastructure.       Create data schemas and models effectively.       Develop and manage data storage solutions including relational databases NoSQL databases Delta Lakes and data lakes.       Validate results with user representatives integrating the overall solution. Influence and enhance customer satisfaction and employee engagement within project teams.

Measures of Outcomes:

TeamOne's Adherence to engineering processes and standards TeamOne's Adherence to schedule / timelines TeamOne's Adhere to SLAs where applicable TeamOne's # of defects post delivery TeamOne's # of non-compliance issues TeamOne's Reduction of reoccurrence of known defects TeamOne's Quickly turnaround production bugs Completion of applicable technical/domain certifications Completion of all mandatory training requirements Efficiency improvements in data pipelines (e.g. reduced resource consumption faster run times). TeamOne's Average time to detect respond to and resolve pipeline failures or data issues. TeamOne's Number of data security incidents or compliance breaches.

Outputs Expected:

Code:

Develop data processing code with guidance
ensuring performance and scalability requirements are met. Define coding standards
templates
and checklists. Review code for team and peers.

Documentation:

Create/review templates
checklists
guidelines
and standards for design/process/development. Create/review deliverable documents
including design documents
architecture documents
infra costing
business requirements
source-target mappings
test cases
and results.

Configure:

Define and govern the configuration management plan. Ensure compliance from the team.

Test:

Review/create unit test cases
scenarios
and execution. Review test plans and strategies created by the testing team. Provide clarifications to the testing team.

Domain Relevance:

Advise data engineers on the design and development of features and components
leveraging a deeper understanding of business needs. Learn more about the customer domain and identify opportunities to add value. Complete relevant domain certifications.

Manage Project:

Support the Project Manager with project inputs. Provide inputs on project plans or sprints as needed. Manage the delivery of modules.

Manage Defects:

Perform defect root cause analysis (RCA) and mitigation. Identify defect trends and implement proactive measures to improve quality.

Estimate:

Create and provide input for effort and size estimation
and plan resources for projects.

Manage Knowledge:

Consume and contribute to project-related documents
SharePoint
libraries
and client universities. Review reusable documents created by the team.

Release:

Execute and monitor the release process.

Design:

Contribute to the creation of design (HLD
LLD
SAD)/architecture for applications
business components
and data models.

Interface with Customer:

Clarify requirements and provide guidance to the Development Team. Present design options to customers. Conduct product demos. Collaborate closely with customer architects to finalize designs.

Manage Team:

Set FAST goals and provide feedback. Understand team members' aspirations and provide guidance and opportunities. Ensure team members are upskilled. Engage the team in projects. Proactively identify attrition risks and collaborate with BSE on retention measures.

Certifications:

Obtain relevant domain and technology certifications.

Skill Examples:

      Proficiency in SQL Python or other programming languages used for data manipulation.       Experience with ETL tools such as Apache Airflow Talend Informatica AWS Glue Dataproc and Azure ADF.       Hands-on experience with cloud platforms like AWS Azure or Google Cloud particularly with data-related services (e.g. AWS Glue BigQuery).       Conduct tests on data pipelines and evaluate results against data quality and performance specifications.       Experience in performance tuning.       Experience in data warehouse design and cost improvements.       Apply and optimize data models for efficient storage retrieval and processing of large datasets.       Communicate and explain design/development aspects to customers.       Estimate time and resource requirements for developing/debugging features/components.       Participate in RFP responses and solutioning. Mentor team members and guide them in relevant upskilling and certification.

Knowledge Examples:

Knowledge Examples

      Knowledge of various ETL services used by cloud providers including Apache PySpark AWS Glue GCP DataProc/Dataflow Azure ADF and ADLF.       Proficient in SQL for analytics and windowing functions.       Understanding of data schemas and models.       Familiarity with domain-related data.       Knowledge of data warehouse optimization techniques.       Understanding of data security concepts. Awareness of patterns frameworks and automation practices.

Additional Comments:

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.