Data Analyst/CDP Developer (SQL/CDP Integration) Location: Torrance, CA (Hybrid of 4 days/week onsite along with 1 day/remote) Contract Duration - March 2026 - March 31, 2028 As a Data Analyst / CDP ...
Data Analyst/CDP Developer (SQL/CDP Integration) Location: Torrance, CA (Hybrid of 4 days/week onsite along with 1 day/remote) Contract Duration - March 2026 - March 31, 2028 As a Data Analyst / CDP ...
Good SQL skills - should know how to debug data issues, knowledge if joins, views etc.
Good SQL skills - should know how to debug data issues, knowledge if joins, views etc.
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SQL Data Tester
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SQL Database Analyst
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SQL DATA Analyst information
See salary details
$21.5K - $31.1K
1% of jobs
$31.1K - $40.7K
0% of jobs
$40.7K - $50.3K
2% of jobs
$50.3K - $59.9K
7% of jobs
$59.9K - $69.5K
8% of jobs
$72K is the 25th percentile. Wages below this are outliers.
$69.5K - $79K
23% of jobs
The median wage is $83K / yr.
$79K - $88.6K
19% of jobs
$97.7K is the 75th percentile. Wages above this are outliers.
$88.6K - $98.2K
15% of jobs
$98.2K - $107.8K
12% of jobs
$107.8K - $117.4K
4% of jobs
$117.4K - $127K
8% of jobs
$21.5K
$87.6K
$127K
How much do sql data analyst jobs pay per year?
What is an SQL Data Analyst?
What is the difference between Sql Data Analyst vs Data Engineer?
| Aspect | Sql Data Analyst | Data Engineer |
|---|---|---|
| Required Skills | SQL, data visualization, reporting | SQL, programming, data pipeline development |
| Certifications | SQL certifications, data analysis courses | Cloud certifications, programming certifications |
| Work Environment | Business intelligence teams, data analysis departments | Data infrastructure teams, software engineering environments |
| Industry Usage | Finance, marketing, healthcare | Tech, finance, large-scale data platforms |
Sql Data Analysts focus on interpreting data, creating reports, and visualizations using SQL. Data Engineers build and maintain data pipelines and infrastructure to support data analysis. While both roles require SQL skills, Data Engineers typically have programming expertise and work on data architecture, whereas Sql Data Analysts concentrate on analyzing and presenting data insights.
What Does an SQL Data Analyst Do?
A SQL data analyst is a data scientist that uses the SQL language to interpret data. This analysis is used to improve business information and provide new insights to managers. Your primary responsibilities revolve around creating these analysis reports, but SQL analytical skills are in demand in many different industries, so specific duties can vary. In many positions, you may be tasked with database upkeep as well, developing infrastructure that increases system efficiency. Regarding academic qualifications, this career requires a bachelor’s degree in either computer science, electrical engineering, information technology, or statistics. Most aspiring SQL analysts gain experience through internships while pursuing their degree.
What are the key skills and qualifications needed to thrive as a SQL Data Analyst, and why are they important?
How does a SQL Data Analyst typically collaborate with cross-functional teams to deliver insights?
Other
Posted 9 days ago
Job description
Data Analyst/CDP Developer (SQL/CDP Integration)
Location: Torrance, CA (Hybrid of 4 days/week onsite along with 1 day/remote)
Contract Duration – March 2026 – March 31, 2028
As a Data Analyst / CDP Developer, this person will support the customer data platform, improve data quality, implement scalable data solutions, and address end to end data challenges. This role requires advanced SQL development, CDP integration, pipeline operations, and troubleshooting across large scale datasets. The ideal candidate is highly analytical, detail oriented, and thrives in a fast paced, cross functional environment.
Responsibilities will include the following:
Data Analysis and Development
- Analyze customer data, event, and large-scale datasets to generate insights, validate data quality, and support business decision-making.
- Write, optimize, and maintain advanced SQL queries across Presto, Hive, and NoSQL systems.
- Use Python to support data processing, automation, workflow operations, and API integrations.
- Work within cloud platforms (primarily AWS) and apply general cloud data architecture principles.
- Perform exploratory data analysis to validate assumptions, identify anomalies, and support stakeholder requirements.
- Document data processes, business rules, logic, and workflows in Confluence or similar platforms.
- Collaborate with engineering, analytics, marketing, CRM teams, and SMEs to define data requirements and ensure accuracy.
- Operate CDP workflow orchestration tools (e.g., Digdag, TD Workflows) to manage pipelines and scheduled jobs.
- Enhance and monitor data quality frameworks including validation checks, alerts, and pipeline observability.
- Follow CI/CD and Git-based version control best practices for SQL, scripts, and documentation.
- Ensure adherence to privacy and compliance standards (GDPR, CCPA, PII).
- Solve end-to-end data challenges—from ingestion through transformation to analysis and activation.
- Maintain rigorous attention to detail, especially with customer-level and production data.
- Proactively improve automation, data quality, workflow reliability, and system performance.
- Communicate insights and technical concepts clearly to both technical and non-technical audiences.
- Partner with SMEs to understand business context, refine requirements, and validate solution accuracy.
Troubleshooting & Support:
- Diagnose and resolve data pipeline failures, ingestion issues, and workflow errors in a timely manner.
- Perform root cause analysis using logs, pipeline metadata, and system monitoring tools.
- Support production environments by monitoring scheduled jobs, validating outputs, and ensuring data accuracy and availability.
- Implement fixes, patches, and process improvements to prevent recurring problems.
- Maintain clear documentation of issues, resolutions, and preventive actions to strengthen operational reliability.
Required Skills and Expertise:
- Bachelor''s degree in Data Science, Computer Science, Information Systems, Engineering, or a related field.
- 5+ years of combined experience across data analysis, data engineering, or data operations.
- Advanced SQL and Query Optimization (Presto, Hive, NoSQL)
- Expertise in writing complex SQL queries optimizing them for large-scale datasets.
- Experience with Customer Data Platforms (CDP) and CRM Systems
- Hands-on experience integrating and managing CDP solutions and CRM platforms to unify and activate customer data.
- Strong SQL expertise, including complex joins, window functions, CTEs, performance optimization.
- Proficiency with Python for data processing, automation, and APIs.
- Experience working with customer data, event data, and large-scale datasets.
- Familiarity with cloud environments such as AWS, Google Cloud Platform, or Azure (S3, IAM, data storage concepts).
- Ability to perform exploratory analysis, validate data quality, and present insights to stakeholders.
- Understanding of data modeling, schema design, identity resolution, and profile unification.
- Comfortable documenting processes, transformations, and analytical logic in Confluence or similar platforms.
- Strong communication skills and the ability to work with cross-functional teams
- Hands-on experience with Treasure Data CDP or similar CDPs (Informatica, Adobe, Salesforce, mParticle, Tealium).
- Experience with workflow orchestration tools (Digdag, TD Workflows).
- Experience with data quality frameworks, monitoring, alerting, and pipeline observability.
- Experience with CI/CD, Git, and version control best practices.
- Understanding of privacy and compliance (GDPR, CCPA, PII handling).
- Skilled in providing support to a production environment.
- Strong problem-solving skills and the ability to deliver end-to-end solutions—from data ingestion to analysis.
- Excellent attention to detail, especially when working with customer-level and production data.
- Proactive mindset for improving data quality, automation, and system reliability.
- Ability to collaborate closely with Subject Matter Experts (SMEs) to understand domain context, validate requirements, and ensure the accuracy and alignment of data solutions.