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:

Mandatory Skills: Database Architect, python, database design, aws, Fast Api Skill to Evaluate: Database Architect, python, database design, aws, Fast Api Experience:8 to 12 Years Location:Bengaluru Job Description: MUST-HAVE: Overall technology experience of 8+ years MUST-HAVE: Minimum experience of 5 years in data modelling and database design MUST-HAVE: Minimum experience of 7 years in designing, implementing, and supporting medium to large scale database systems MUST-HAVE: Minimum experience of 5 years in designing, developing, and supporting solutions using S3, Aurora, any of the Managed RDS MUST-HAVE: Minimum experience of 4 years designing, developing, and tuning solutions using AWS database and storage technologies Solid knowledge of workings of a distributed database models including SQL, No-SQL and performance optimization Solid knowledge of AWS Solution, Aurora database and data technologies Solid knowledge of Fast API design & develop Prior experience with designing, developing, and supporting solutions using database technologies like MySQL, PostgreSQL is a plus Advanced python programming skills is a plus Education Qualificaiton: Overall technology experience of 8+ years, Bachelor's or Master's degree in Computer Science, Engineering, or a related field Roles & Responsibilities: Understand the business domain, core data objects, data entities. Model the relationships between the various entities Design and implement scalable and efficient database solutions using Amazon Aurora. Optimize database performance through indexing, query optimization, and resource management. Collaborate with development teams to integrate Aurora databases with applications. Induct aspects of high performance, security, usability, operability, maintainability, traceability, observability, evolvability into the systems design Assess performance influencing parameters like normalization, de-normalization, most executed transactions, record count, data size, I/O parameters at the database and OS level in the database and table designs Maintain a catalog of meta, master, transactional and reference data Tune the transactions and queries and determine the use of appropriate client libraries and fetch mechanism (like query vs stored procedures) Design the system for resilience, fail-over, self-healing and institute rollback plans Design, develop, and maintain high-performance APIs using FAST API. Develop and test database code and other core and helper utilities in Python Develop and profile queries, triggers, indices, and stored procedures Monitor the health of queries and identify patterns leading to bottlenecks in the system before the customer finds it Own the DevOps and release mgmt. practices pertaining to the database solutions Estimate the cost of AWS services usage and look to continuously optimize the cost Design and develop data REST API layer on Python