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:

As a Data Engineer, you will be responsible for designing, building and maintaining data pipelines and infrastructure that support the client Data Science team. You will work with Google Cloud Platform (GCP) tools and technologies to collect, store and process data from various sources and formats. You will also write scripts to transform and load data explicitly for use in the machine learning (ML) pipelines process. You will optimize data access and performance for ML applications and ensure data quality and availability for analytics/ML teams. You will collaborate with data scientists, analysts, engineers and other stakeholders to deliver data solutions that enable data-driven decision making and innovation. Key Responsibilities As a Data Engineer for the Data Science team, you will perform the following tasks: • Architect, build and maintain scalable, reliable and secure data pipelines and infrastructure using GCP tools such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, etc. • Write scripts to transform and load data for use in ML pipelines using languages such as Python, SQL, etc. • Optimize data access and performance for ML applications using techniques such as partitioning, indexing, caching, etc. • Ensure data quality and availability for analytics/ML teams using tools such as Data Catalog, Data Studio, Data Quality, etc. • Monitor, troubleshoot and debug data issues and errors using tools such as Stackdriver, Cloud Logging, Cloud Monitoring, etc. • Document and maintain data pipelines and infrastructure specifications, standards and best practices using tools such as Git, Jupyter, etc. • Collaborate with data scientists, analysts, engineers, and other stakeholders to understand data requirements, provide data solutions and support data-driven projects and initiatives. • Support generative AI projects by providing data for training, testing and evaluation of generative models such as Gemini, Claude 3, etc. • Implement data augmentation, data anonymization and data synthesis techniques to enhance data quality and diversity for generative AI projects. Qualifications To be successful in this role, you will need to have the following qualifications: • Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics or related field. • At least years of experience in data engineering, data warehousing, data integration or related field. • Experience 6 in a health care-related field or working with health care data is preferred. • Proficient in GCP tools and technologies for data engineering, such as BigQuery, Dataflow, Dataproc, Pub/Sub, Cloud Storage, etc., or equivalents in other cloud platforms • Proficient in scripting languages such as Python, SQL, etc. for data transformation and loading. • Knowledge of data modeling, data quality, data governance and data security principles and practices. • Knowledge of ML concepts, frameworks and tools such as TensorFlow, Keras, Scikit-learn, etc. • Knowledge of generative AI concepts, frameworks and tools. • Excellent communication, collaboration and problem-solving skills.