Lead 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:
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:
Sr Data Engineer Position Location: OUS with minimum of 6 hrs. overlap with US timings. Must have Skills* 1. 15 years of experience in design and delivery of Distributed Systems capable of handling petabytes of data in a distributed environment years of experience in the development of Data Lakes with Data Ingestion from disparate data sources, including relational databases, flat files, APIs, and streaming data. 3. Experience in providing Design and development of Data Platforms and data ingestion from disparate data sources into the cloud. 4. Expertise in core AWS Services including AWS IAM, VPC, EC2, EKS/ECS, S3, RDS, DMS, Lambda, CloudWatch, CloudFormation, CloudTrail, CloudWatch. 5. Proficiency in programming languages like Python and PySpark to ensure efficient data processing. preferably Python. 6. Architect and implement robust ETL pipelines using AWS Glue, defining data extraction methods, transformation logic, and data loading procedures across different data sources 7. 15 years of Experience in using IaC tools like Terraform etc years of experience in development of CI/CD pipelines (GitHub Actions, Jenkins). 9. Experience in the development of Event-Driven Distributed Systems in the Cloud using Serverless Architecture. 10. Ability to work with Infrastructure team for AWS service provisioning for databases, services, network design, IAM roles and AWS cluster years of experience working with Document DB. 12. Ability to design, orchestrate and schedule jobs using Airflow. 13. Knowledge of AWS AI Services like AWS Entity Resolution, AWS Comprehend. 14. Ability to run custom LLMs using Amazon SageMaker. 15. Ability to use Large Language Models (LLMs) for Data Classification and Identification of PII data entities Nice to have Skills: 1. 10 years of experience in the development of Data Audit, Compliance and Retention standards for Data Governance, and automation of the governance processes. 2. Experience in data modelling with NoSQL Databases like Document DB. 3. Experience in using column-oriented data file format like Apache Parquet, and Apache Iceberg as the table format for analytical datasets. 4. Expertise in development of Retrieval-Augmented Generation (RAG) and Agentic Workflows for providing context to LLMs based on proprietary enterprise data. 5. Ability to develop re-ranking strategies using results from Index and Vector stores for LLMs to improve the quality of the output.

Skills:Data Lake,AWS,Python

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