Business Analyst with Python
Posted 1 day ago by Compugra Systems
Negotiable
Undetermined
Remote
Remote
Summary: The Business Analyst role focuses on data analysis and modeling, requiring expertise in Python and data concepts. The position involves collaborating with data architects and engineers to ensure data quality and governance while supporting business decision-making and AI initiatives. This is a remote position with a contract duration of 6 to 12+ months. Candidates should have 6-8 years of relevant experience.
Key Responsibilities:
- Analyze complex data from multiple source systems to understand entities, relationships, business rules, and usage patterns.
- Contribute to conceptual and logical data models for key domains, working with data architects and modelers.
- Perform detailed data profiling on new and existing data sets to identify issues and opportunities.
- Lead or co-lead source target mapping for new data products and migrations.
- Participate actively in backlog refinement, design reviews, and sprint ceremonies to clarify data requirements.
- Create and maintain high-quality documentation for data definitions and mapping.
Key Skills:
- Proficiency in Python, PL/SQL, and data modeling concepts.
- Experience in data analysis, profiling, and quality assurance.
- Strong understanding of data governance and metadata management.
- Ability to collaborate effectively with cross-functional teams.
- Experience with data mapping and transformation logic.
- Excellent documentation and communication skills.
Salary (Rate): undetermined
City: undetermined
Country: undetermined
Working Arrangements: remote
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Position: Business Analyst
Location: COLUMBUS, OH (Remote)
Duration: 6 to 12+ Months Contract
Job Description:
Skills: Digital : Python~Data Concepts & Data Modelling~PL/SQL
Experience Required: 6-8 Years
Key Responsibilities
1. Data Analysis & Modeling Analyze complex data from multiple source systems to understand entities| relationships| business rules| and usage patterns (e.g.| policy| claims| finance| partner data).
Contribute to conceptual and logical data models for key domains| working in partnership with data architects and data modelers.
Recommend modeling patterns (e.g.| relational| dimensional) appropriate for warehouses| marts| and operational data stores| ensuring models are usable by downstream analytics and AI workloads.
Review and provide feedback on model changes| ensuring that naming| keys| cardinality| and business definitions are consistent and well documented.
2. Data Profiling & Data Quality Perform detailed data profiling on new and existing data sets (nulls| distributions| outliers| code sets| pattern and length analysis) using SQL and other tools to identify issues and opportunities.
Summarize profiling results in clear| consumable formats (tables| visuals| narratives) that explain data quality risks and their impact on business value and delivery scope.
Partner with data stewards| product owners| and engineers to translate profiling findings into data quality rules| checks| and monitoring requirements for pipelines and data products.
Support Trusted Data / AI-ready objectives by ensuring that quality expectations| thresholds| and controls are explicitly defined and testable for the assets you support.
3. Source Target Mapping & Design Lead or co-lead source target mapping for new data products and migrations| especially where legacy systems and cross-domain data need to be reconciled.
Validate mapping choices against business requirements| data types| formats| and constraints; identify and document transformation| derivation| and standardization logic.
Define and document completeness and reconciliation approaches (e.g.| row counts| hash totals| key coverage) that engineers and testers can automate in pipelines and tests.
Work closely with data engineers to ensure mappings are feasible| performant| and aligned to data golden-path patterns from raw harmonized curated zones.
4. Collaboration| Delivery & Run Participate actively in backlog refinement| design reviews| and sprint ceremonies to clarify data requirements| acceptance criteria| and non-functional needs (e.g.| latency| freshness| lineage| access controls).
Provide clear analytical input to estimates| tradeoffs| and risk assessment for stories and epics involving data sourcing| modeling| and mapping.
Support testing by defining test data needs| validating mapping implementations| and reviewing defects related to data issues (values| joins| business rules).
Contribute to incident/root-cause analysis for data issues and partner with engineering teams on durable fixes (not just one-off corrections).
5. Governance| Metadata & Documentation Create and maintain high-quality documentation for data definitions| mapping
Essential Skills: Data Analyst plays a hands-on role in understanding| modeling| and translating complex data from multiple source systems into trusted| analytics-ready data products that support business decision-making| regulatory reporting| and AI/analytics use cases.