Lead II - Data Engineering

Role Description 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 requirementst
• 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.

Code Outputs Expected:

• 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 We are seeking a highly experienced Senior Data Engineer to design, develop, and optimize scalable data pipelines in a cloud-based environment. The ideal candidate will have deep expertise in PySpark, SQL, Azure Databricks, and experience with either AWS or GCP. A strong foundation in data warehousing, ELT/ETL processes, and dimensional modeling (Kimball/star schema) is essential for this role. Must-Have Skills 8+ years of hands-on experience in data engineering or big data development. Strong proficiency in PySpark and SQL for data transformation and pipeline development. Experience working in Azure Databricks or equivalent Spark-based cloud platforms. Practical knowledge of cloud data environments – Azure, AWS, or GCP. Solid understanding of data warehousing concepts, including Kimball methodology and star/snowflake schema design. Proven experience designing and maintaining ETL/ELT pipelines in production. Familiarity with version control (e.g., Git), CI/CD practices, and data pipeline orchestration tools (e.g., Airflow, Azure Data Factory

Skills Azure Data Factory,Azure Databricks,Pyspark,Sql