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Data Science Practitioner Jobs in Alabama (NOW HIRING)

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Data Quality Engineer

Montgomery, AL · On-site

$113.30K - $136K/yr

Analytics & Data Science Skills: * Data Quality Standards & Metrics: Define and enforce data ... DAMA CDMP (Associate/Practitioner) · EDM Council DCAM · ASQ Data Quality Credential · Collibra ...

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How much do data science practitioner jobs pay per year?

As of Jun 1, 2026, the average yearly pay for data science practitioner in Alabama is $99,043.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,313.00 and $132,351.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Data Science Practitioner, and why are they important?

To thrive as a Data Science Practitioner, you need strong statistical analysis, programming skills (often in Python or R), and a solid understanding of machine learning algorithms, typically supported by a degree in computer science, statistics, or a related field. Familiarity with data visualization tools (such as Tableau or Power BI), big data platforms (like Hadoop or Spark), and relevant certifications (e.g., Google Data Analytics, Microsoft Certified: Data Scientist Associate) is highly beneficial. Critical thinking, problem-solving, and effective communication skills help translate complex data insights into actionable business strategies. These skills and qualities are crucial for extracting value from data and driving data-informed decision-making in organizations.

How do Data Science Practitioners typically collaborate with cross-functional teams to drive project success?

Data Science Practitioners often work closely with professionals from engineering, product management, and business analytics to ensure that data-driven solutions align with organizational goals. They participate in regular meetings to understand project requirements, share insights from data analyses, and help translate complex technical findings into actionable strategies for stakeholders. Effective communication and the ability to explain technical concepts to non-technical teammates are essential, as is adapting to rapidly changing project priorities. Collaboration not only enhances project outcomes but also creates opportunities for learning and career advancement.

What are Data Science Practitioners?

Data Science Practitioners are professionals who use scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. They analyze large datasets to identify trends, build predictive models, and help organizations make data-driven decisions. Their work often involves programming, statistics, and domain expertise across various industries such as finance, healthcare, and technology.

What is the difference between Data Science Practitioner vs Data Analyst?

AspectData Science PractitionerData Analyst
Required CredentialsBachelor's or higher in Data Science, Statistics, or related fields; certifications like Certified Data ScientistBachelor's in Statistics, Mathematics, or related fields; certifications like Microsoft Data Analyst
Work EnvironmentDevelops models, algorithms, and predictive analytics; often involves programming and machine learningPerforms data cleaning, visualization, and reporting; focuses on descriptive analytics
Employer & Industry UsageTech companies, finance, healthcare, and consulting firmsBusiness, marketing, finance, and healthcare sectors

While both roles analyze data, Data Science Practitioners focus on building predictive models and advanced analytics, whereas Data Analysts primarily interpret existing data to generate reports and insights. The roles often overlap but differ in technical depth and scope.

What are popular job titles related to Data Science Practitioner jobs in Alabama? For Data Science Practitioner jobs in Alabama, the most frequently searched job titles are:
What job categories do people searching Data Science Practitioner jobs in Alabama look for? The top searched job categories for Data Science Practitioner jobs in Alabama are:
Data Science Practitioner jobs near you
Infographic showing various Data Science Practitioner job openings in Alabama as of May 2026, with employment types broken down into 1% As Needed, 84% Full Time, 14% Part Time, and 1% Contract. Highlights an 86% Physical, 2% Hybrid, and 12% Remote job distribution, with an average salary of $99,043 per year, or $47.6 per hour.
Data Quality Engineer

Data Quality Engineer

SYSTEM SOFT TECHNOLOGIES LLC

Montgomery, AL

$113.30K - $136K/yr

Other

Posted 9 days ago

Job description

Data Quality Engineer

Location: Montgomery, AL (Onsite)

Duration: 4 Months+ (This will be a long-term, one-year renewable) (Contract Hire)

In summary: A Data Quality Engineer, strong data analyst with deep technical skills in SQL, Purview, Data Pipelines and Data Modeling, plus experience in cloud data environments, automated testing, and collaboration with analytics and engineering teams. Ensures data is not only clean but also ready to support advanced analytics and AI applications

Data Quality Engineer & Analytics Skills

Core Technical Skills: MUST BE ABLE TO NAVIGATE AN ENVIRONMENT WITH LOW\NO DATA MATURITY

Data Profiling & Cleansing: Analyze data to identify anomalies, duplicates, outliers, and missing values; apply cleansing techniques to improve data integrity.

SQL Proficiency: Write complex queries to validate data accuracy, perform transformations, and generate reports. (SSIS - ETL\ELT)

Python & Other Languages: Python is widely used for automation, data validation, and integration with analytics pipelines; SQL is essential for querying and reporting.

Data Modeling & Warehousing: Understand ETL/ELT processes, data warehouse/lake/lakehouse architectures, and data modeling principles.

Cloud & Modern Data Stack: Experience with cloud platforms (AWS, Google Cloud Platform, Azure), modern data warehouses (Snowflake, BigQuery), and tools like Spark, Kafka/Kinesis, Hadoop, or S3.

Data Testing & Observability: Design and deploy automated data testing at scale; use observability platforms for real-time monitoring.

Analytics & Data Science Skills

Data Quality Standards & Metrics: Define and enforce data quality benchmarks; measure completeness, accuracy, timeliness, and consistency.

Root Cause Analysis: Identify why data issues occur (ETL bugs, user input errors, system failures) and implement fixes.

Collaboration with Data Scientists: Work with ML/data science teams to ensure training data is clean and reliable.

Statistical & Trend Analysis: Interpret patterns in large datasets to inform quality improvements.

Soft & Communication Skills

Stakeholder Engagement: Gather requirements from business, engineering, and analytics teams; advocate for data quality across the organization.

Problem-Solving & Attention to Detail: Spot and resolve data issues efficiently; maintain high precision in validation.

Documentation: Record quality issues, processes, and improvements for transparency and compliance.

Tools & Platforms

Query & Analysis: SQL, Python, Spark, Kafka/Kinesis, Hadoop, S3.

Data Quality Tools: Data profiling tools (MS Purview), validation scripts, observability platforms.

Collaboration: Jira, Snowflake, or other data governance platforms.

Required Skills:

  • Strong experience working in low or immature data environments, establishing data quality processes from scratch (8-10 Years)
  • Advanced SQL expertise for complex querying, data validation, and transformation (8-10 Years)
  • Hands-on experience with ETL/ELT pipelines (e.g., SSIS or similar tools) (8-10 Years)
  • Proficiency in Python for data automation, validation, and pipeline integration (5-8 Years)
  • Experience with data profiling and cleansing (anomalies, duplicates, outliers, missing values) (8-10 Years)
  • Solid understanding of data modeling and data warehouse/lake/lakehouse architectures (8-10 Years)
  • Experience implementing data quality frameworks and metrics (accuracy, completeness, timeliness, consistency) (8-10 Years)
  • Experience with cloud data platforms (AWS, Azure, or Google Cloud Platform) and modern data warehouses (e.g., Snowflake, BigQuery) (5-8 Years)
  • Required Tools & Platforms: (8-10 Years) Query & Analysis: SQL, Python, Spark, Kafka/Kinesis, Hadoop, S3. Data Quality Tools: Data profiling tools (MS Purview), validation scripts, observability platforms. Collaboration: Jira, Snowflake, or other data governance platforms.
  • Bachelor's Degree

Preferred Skills:

  • Knowledge of DAMA-DMBoK, DCAM, MDM concepts, and governance frameworks. (8-10 Years)
  • Experience with Microsoft Purview, Fabric, MS Power BI, and Key Vault (5-8 Years)
  • Familiarity with AI/ML data readiness and feature-store-aligned data structuring. (5-8 Years)
  • Cloud data engineering exposure (Azure, Databricks, Google Cloud Platform). (5-8 Years)
  • Master's degree preferred.
  • DAMA CDMP (Associate/Practitioner) EDM Council DCAM ASQ Data Quality Credential Collibra Data Steward Certification Certified Data Steward (eLearningCurve) Cloud/AI certifications (Azure, Databricks, Google)

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