Data Analyst Job Jamestown area,Town of Poland New York USA,IT/Tech

Location: Town of Poland

Data Analysis & Profiling:
Perform deep-dive data analysis, data profiling, data validation, and quality assessments to ensure data accuracy, consistency, and compliance.
Data Modeling:
Design and implement conceptual, logical, and physical data models for enterprise data platforms (Data Warehouses, Data Lakes, and Lakehouse architectures).
Requirements Gathering:
Collaborate closely with business stakeholders, data architects, and analytics teams to gather and translate complex business requirements into scalable, clean data structures.
Collaboration with DE Teams:
Work hand-in-hand with ETL/ELT and Data Engineering teams to ensure proper data mapping, transformation rules, and seamless integration across multiple source systems.
Data Governance & Documentation:
Maintain comprehensive metadata, data lineage, data dictionaries, and technical documentation. Support master data management (MDM) and data governance initiatives.
Performance Optimization:
Optimize database performance from an analytical perspective through indexing strategy, partitioning recommendations, and query tuning.
Compliance:
Ensure alignment with enterprise data standards, governance policies, and strict healthcare regulations such as GxP, HIPAA, and GDPR (highly applicable in Pharma/Life Sciences).
Agile Delivery:
Participate actively in Agile/Scrum ceremonies and collaborate within cross-functional delivery teams.

Required Skills & Experience (Must Have)

Core Profile:
Proven, strong experience working strictly as a Data Analyst and/or Data Modeler in enterprise environments.
Data Modeling Expertise:
Hands‑on expertise in relational and dimensional data modeling (Star/Snowflake schemas). Proficiency in data modeling tools such as ERwin, ER/Studio, Power Designer, or similar.
Advanced SQL:
Expert‑level SQL programming, analytical functions, and query optimization skills.
Data Architecture Knowledge:
Deep understanding of data warehousing, data lakes, and lakehouse concepts from a data structure perspective.
Database Familiarity:
Hands‑on experience navigating and querying databases such as SQL Server, Postgre

SQL, Snowflake, Oracle, or Redshift.
Business Acumen:
Exceptional ability to translate chaotic business needs into structured data requirements and source‑to‑target mappings.
Agile & Tools:
Experience working in Agile/Scrum environments and using version control tools (Git).

Good to Have / Preferred Technical Skills (Data Engineering Exposure)

Note:

The following skills are considered a plus to help you collaborate with engineering teams, but they do not replace the core data analysis requirements.
Data Pipelines & Transformation:
Experience with DBT or hands‑on exposure to ETL/ELT tools (Informatica, Talend, Azure Data Factory, AWS Glue).
Cloud Platforms:
Exposure to AWS data services (S3, Redshift, Lambda, Athena) or similar Azure/GCP solutions.
Programming for Analytics:
Experience with Python or PySpark for data analysis, profiling, and automation.
Big Data & Streaming:
Familiarity with Databricks, Spark, or Kafka.
BI Tools:
Exposure to BI and reporting tools such as Power BI, Tableau, or Quick Sight.
Advanced Modeling:
Understanding of Data Vault 2.0 modeling concepts.
Domain Knowledge:
Prior experience in Pharma, Healthcare, or Life Sciences domains is highly preferred and will be a significant advantage.
Dev Ops Basics:
Basic awareness of CI/CD pipelines (Jenkins, Git Hub Actions, Dev Ops) and data infrastructure.

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