Data Analyst (Azure Databricks)

Job Description

Job Description

Data Analyst – (Azure Databricks)

Remote

Rate: $55/hr on C2C

PSL303558-155-1

Role Summary

We are seeking an experienced Senior Data Analyst to drive data analysis, source-to-target mapping, and business-driven data insights across complex healthcare datasets, with a strong focus on claims data. This role will partner closely with business stakeholders, data engineering teams, and analytics initiatives to ensure high-quality, well-governed, and analysis-ready data to support forecasting, reporting, and AI/ML use cases.

The ideal candidate will combine strong SQL and data analysis expertise, deep healthcare domain knowledge (claims, provider, member data), and hands-on experience with Azure Databricks and modern data platforms.

Key Responsibilities:

1. Data Analysis & Mapping

a) Perform initial data analysis and profiling of complex healthcare datasets (claims, providers, members, finance).

b) Create and maintain Source-to-Target Mapping (STM) documents, ensuring traceability and accuracy across systems.

c) Define and standardize data definitions, business glossary, transformations, and business rules.

d) Identify data anomalies, trends, and inconsistencies and work towards resolution.

2. Business Engagement & Intake

a) Collaborate closely with business stakeholders to understand requirements and translate them into analytical solutions.

b) Define and manage data intake processes, onboarding new datasets efficiently.

c) Act as a bridge between business, SME, data engineering and data science teams for requirement clarification and validation.

3. SQL & Data Processing

a) Write and validate complex, optimized SQL queries for data extraction, transformation, and validation.

b) Work with large datasets using Azure Databricks (Spark, SQL, Python/Scala).

c) Support and validate data pipeline outputs developed in Databricks.

d) Experience working with Snowflake and cloud-based data warehouses is a plus.

e) Work with engineering teams to ensure scalable and secure data solutions.

4. Healthcare Data Expertise

a) Analyze and handle healthcare claims data, ensuring compliance with domain-specific business rules.

b) Work with datasets across Claims, Provider, Member, Vision, Dental, and Finance domains.

c) Ensure correct interpretation of healthcare data structures and coding standards.

5. Data Quality & Governance

a) Ensure high standards of data quality, consistency, and governance.

b) Develop and enforce validation frameworks and reconciliation processes.

c) Document data lineage, definitions, and transformation logic.

Required Skills:

1. Technical Skills

a) Strong experience in SQL (advanced querying, optimization)

b) Hands-on experience with Azure Databricks (Spark, Delta Lake)

c) Experience with cloud data platforms (Azure, Snowflake, BigQuery is a plus)

d) Knowledge of data modeling, ETL/ELT concepts, and data warehousing

2. Domain Expertise

a) Strong experience in healthcare data, especially claims data processing

b) Understanding of provider, member, financial datasets

c) Familiarity with healthcare data standards and business rules

3. Analytical & Functional Skills

a) Expertise in data profiling, root cause analysis, and validation

b) Experience creating Source-to-Target Mapping documents

c) Ability to work with ambiguous requirements and drive clarity

d) Strong problem-solving and troubleshooting skills

4. Soft Skills

a) Excellent communication and stakeholder management

b) Ability to work cross-functionally with business and technical teams

c) Strong ownership and accountability mindset

Required Qualification:

• Overall 8 years of experience.

• Minimum of 3 years of experience in Azure (ADF), Databricks

• 5 years of experience in writing advanced level SQL.

Key Outcomes / Success Metrics

• Improved data quality, consistency, and governance

• Faster onboarding of new datasets and reduced dependency on engineering teams

• Enhanced forecast accuracy and analytics reliability

• Reduced manual data preparation effort

• Delivery of business-ready, analysis-ready datasets for advanced analytics

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