Senior Data Analyst – Enterprise Data & Insights
We are seeking a highly analytical Senior Data Analyst to work with large, complex enterprise datasets and turn ambiguous data into meaningful insights and actionable business recommendations.
This role is ideal for someone who enjoys digging into messy data, figuring out what can be trusted, identifying patterns and discrepancies, and translating findings into a clear story for business and technical stakeholders.
This is an analytics-focused role, not a Data Engineering position. While you will work within an environment that includes Databricks, Spark, ETL processes, and enterprise data infrastructure, your focus will be on analyzing, investigating, validating, and interpreting data—not building production data pipelines.
What You’ll Do
Analyze large, complex enterprise datasets using SQL and Databricks
Investigate data quality, coverage, duplication, inconsistencies, discrepancies, and edge cases
Determine which data is reliable and apply business logic when source data is incomplete, inconsistent, or poorly documented
Identify meaningful patterns, trends, and opportunities within the data
Define, analyze, and validate business metrics and KPIs
Translate analytical findings into clear insights and actionable recommendations
Develop compelling data stories that explain not only what the data shows, but why it matters
Compare and reconcile data across multiple systems and sources
Support analysis related to enterprise data migrations, transformations, and validation
Partner closely with business and technical stakeholders to understand questions, investigate issues, and communicate findings
Work within data environments involving Databricks, Spark, ETL processes, and complex enterprise data flows
What We’re Looking For
5–10 years of experience in data analytics, business intelligence, data analysis, or a similar analytical role
Bachelor’s degree in Computer Science, Technology, Data Analytics, or a related field
Advanced SQL skills with experience querying and analyzing large enterprise datasets
Strong analytical and investigative problem-solving skills
Proven ability to work with large, messy, or ambiguous datasets
Experience identifying and resolving data quality, duplication, coverage, and consistency issues
Ability to take an ambiguous business problem and determine how to use available data to answer it
Experience deriving insights and translating them into business recommendations
Strong data storytelling and communication skills
Ability to explain complex findings clearly to both technical and non-technical stakeholders
Strong attention to detail and willingness to investigate anomalies and edge cases
Preferred Experience
Hands-on Databricks experience for data exploration, investigation, and analytics
Working knowledge of Spark
Understanding of ETL processes, data flows, and data validation
Experience supporting enterprise data migrations or complex data environments
Experience developing or analyzing KPIs and business metrics
Familiarity with graph-style data models, knowledge graphs, or ontologies is a plus
What This Role Is — and Isn’t
The strongest candidate is an analytics professional first who also understands the technical environment surrounding enterprise data.
You should be comfortable with Databricks, Spark, ETL concepts, and data flows, but your primary strength should be investigating data and extracting insights from it.
This role is not focused on:
Building production ETL pipelines
Data pipeline engineering
Data synchronization
Infrastructure engineering
Production performance optimization
Traditional Data Engineering ownership
If your strength is taking complex data, determining what can be trusted, uncovering meaningful insights, and communicating what the business should do with those insights, this role could be a strong fit.
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