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Lead II - Data Engineering -Python
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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.
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:
# of Resources: 22 Role(s): Technical Role Location(s): India Planned Start Date: 1/1/2026 Planned End Date: 6/30/2026 Project Overview: Role Scope / Deliverables: We are seeking highly skilled Data Engineer with strong experience in Databricks, PySpark, Python, SQL, and AWS to join our data engineering team on or before 1st week of Dec, 2025 . The candidate will be responsible for designing, developing, and optimizing large-scale data pipelines and analytics solutions that drive business insights and operational efficiency . Design, build, and maintain scalable data pipelines using Databricks and PySpark. Develop and optimize complex SQL queries for data extraction, transformation, and analysis. Implement data integration solutions across multiple AWS services (S3, Glue, Lambda, Redshift, EMR, etc.). Collaborate with analytics, data science, and business teams to deliver clean, reliable, and timely datasets. Ensure data quality, performance, and reliability across data workflows. Participate in code reviews, data architecture discussions, and performance optimization initiatives. Support migration and modernization efforts for legacy data systems to modern cloud-based solutions. Key Skills: Hands-on experience with Databricks , PySpark & Python for building ETL/ELT pipelines. Proficiency in SQL (performance tuning, complex joins, CTEs, window functions). Strong understanding of AWS services (S3, Glue, Lambda, Redshift, CloudWatch, etc.). Experience with data modeling, schema design, and performance optimization. Familiarity with CI/CD pipelines, version control (Git), and workflow orchestration (Airflow preferred). Excellent problem-solving, communication, and collaboration skills.
Skills
Databricks,Pyspark & Python,Sql,Aws Services
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