Python Senior Technical Lead - Data Analysis, SQL

Job Summary

This position entails leading technical projects in the areas of SQL, data analysis, and Python. The Senior Technical Lead will be responsible for overseeing the development and implementation of technical solutions that leverage SQL databases, perform data analysis, and utilize Python programming for automation and data manipulation tasks.

Key Responsibilities

1. Lead technical projects involving sql, data analysis, and python from inception to implementation.
2. Design and develop sql databases, queries, and stored procedures to support various applications.
3. Conduct data analysis to derive valuable insights and make data driven decisions.
4. Develop and maintain python scripts to automate tasks and streamline processes.
5. Collaborate with cross functional teams to integrate technical solutions and ensure compatibility.
6. Troubleshoot technical issues related to sql databases, data analysis, and python scripts.
7. Stay updated on emerging technologies and recommend enhancements to existing systems.

Skill Requirements

 
JD for Data Analyst
1.    Business & Domain Understanding
o    Translating business requirements into data structures and mappings
o    Working with SMEs to define business terms and KPIs
o    Understanding how data is consumed (reports, dashboards, analytics, APIs) and designing models accordingly
2.    Data Modeling & Mapping
o    Conceptual, logical, and physical data models; Normalization & Denormalization and when to use each
o    Entity-relationship (ER) modelling; Dimensional modelling;Slowly Changing Dimensions (SCD types)
o    Understand existing data model and alignment; work experience in ERwin,Maintaining models as living artefacts (versioning, change tracking)
o   Source-to-target mapping (STM) / mapping specifications; Maintain Data Mapping & data dictionary document aligning standards.
o   Understanding of source systems: APIs, databases, flat files, messages
o   Mapping business concepts to data elements and attributes;Handling data transformations: cleansing, standardization, enrichment
o    Understanding how models and mappings are implemented in ETL;How mapping specs are handover docs for ETL developers / engineers
3.    SQL & Querying
o   Strong SQL for exploring data and validating models;Joins, aggregations, window functions
o   Strong data profiling to refine models and mappings
4.    Testing & Data Validation
o   Documenting validation rules, thresholds, and exception criteria;Translating business and technical requirements into test cases
o   Defining test scenarios, test data, expected results; Executing manual test cases, validating results, logging defects
o   Using or scripting automation (e.g., SQL scripts, Python, or test tools) for repeatable checks
o   Perform Data validation, Support UAT & UVT 
o   Test cases tracking and update in ADO; Incident Handling
5.    Data Quality, Standards & Governance
o   Data quality dimensions: accuracy, completeness, consistency, timeliness, uniqueness
o   Designing models that support data quality checks and constraints
o   Metadata: business glossary, data catalog, data lineage
o   Naming conventions, standards for keys, data types, and codes
6.    Documentation & Communication
o   Clear mapping &model documents: field-level mappings, transformation rules, validation rules
o   Communicating models and mappings to business stakeholders and technical teams
o   Participating in design and review workshops

Other Requirements

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