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Datastage Developer Jobs in Virginia (NOW HIRING)

Omm IT Solutions

Sr Data Engineer

Mclean, VA

$116K - $139K/yr

Hands\-on experience with legacy ETL frameworks (e.g., IBM DataStage) to support modernization initiatives. \n * DevOps & CI\/CD: Familiarity with continuous integration and continuous deployment ...

Acumen Solutions

Data Analyst

Mclean, VA

Bachelor's degree or equivalent in Computer Science, Information Systems, Engineering, Business ... Informatica, Pentaho, DataStage, Jitterbit, Mulesoft, etc. * Relevant IT certifications preferred ...

Acumen Solutions

Data Analyst

Mclean, VA · On-site

Bachelor's degree or equivalent in Computer Science, Information Systems, Engineering, Business ... Informatica, Pentaho, DataStage, Jitterbit, Mulesoft, etc. * Relevant IT certifications preferred ...

Acumen Solutions

Data Analyst

Mclean, VA

Bachelor's degree or equivalent in Computer Science, Information Systems, Engineering, Business ... Informatica, Pentaho, DataStage, Jitterbit, Mulesoft, etc. * Relevant IT certifications preferred ...

Acumen Solutions

Data Analyst

Mclean, VA · On-site

Bachelor's degree or equivalent in Computer Science, Information Systems, Engineering, Business ... Informatica, Pentaho, DataStage, Jitterbit, Mulesoft, etc. * Relevant IT certifications preferred ...

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All JobsDatastage Developer JobsDatastage Developer Jobs in Virginia

Datastage Developer information

See Virginia salary details

$10

$57

$129

How much do datastage developer jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for datastage developer in Virginia is $57.15, according to ZipRecruiter salary data. Most workers in this role earn between $48.61 and $61.49 per hour, depending on experience, location, and employer.

What is a Datastage developer?

A DataStage developer is in charge of using IBM InfoSphere DataStage. This is an ETL tool that extracts, transforms, and applies data to specific business goals. In this career, you manage the server and use the software to develop and implement DataStage and ETL tests for your organization. Other job duties include providing technical assistance to team members, coordinating and scheduling all tasks, and reviewing data integration plans and procedures. The career requires extensive work experience with DataStage and similar ETL tools. Additional qualifications are strong technical knowledge, communication skills, and problem-solving abilities.

How does a Datastage developer typically collaborate with data architects and business analysts during a project?

Datastage Developers frequently work alongside data architects to understand the overall data model and technical requirements for ETL processes. They also interact with business analysts to clarify business rules and ensure data transformations meet organizational needs. Effective communication and documentation are crucial, as developers must translate business requirements into technical specifications and maintain ongoing feedback loops throughout the project lifecycle. This collaborative structure ensures that the ETL solutions are both technically sound and aligned with business objectives.

What are the key skills and qualifications needed to thrive as a Datastage developer, and why are they important?

To thrive as a Datastage Developer, you need strong expertise in ETL processes, SQL, and data warehousing concepts, usually supported by a degree in computer science or a related field. Familiarity with IBM DataStage, version control systems, and related database platforms such as Oracle or DB2 is typically required, with certifications in DataStage or IBM InfoSphere being advantageous. Analytical thinking, attention to detail, and effective communication skills set top performers apart in this role. These skills and qualities are crucial for building robust, scalable data integration solutions that meet business requirements and ensure data quality.

What is the difference between Datastage Developer vs ETL Developer?

AspectDatastage DeveloperETL Developer
CertificationsIBM DataStage certifications, SQL, ETL toolsETL tools certifications (Informatica, Talend), SQL
Work EnvironmentPrimarily uses IBM DataStage in data warehousing projectsUses various ETL tools across different platforms
Industry UsageCommon in banking, finance, healthcareWidespread across industries using ETL processes
Job FocusDesigning and developing data integration solutions with DataStageBuilding and maintaining ETL workflows with various tools

While both roles involve data integration and ETL processes, a Datastage Developer specializes in IBM DataStage, whereas an ETL Developer may work with multiple ETL tools. The choice depends on the company's technology stack and project requirements.

More about Datastage Developer jobs

What are the most commonly searched types of Datastage Developer jobs in Virginia?

The most popular types of Datastage Developer jobs in Virginia are:

What cities in Virginia are hiring for Datastage Developer jobs?

Cities in Virginia with the most Datastage Developer job openings:

What are popular job titles related to Datastage Developer jobs in VA?

For Datastage Developer jobs in VA, the most frequently searched job titles are:

Datastage Developer jobs near you
Infographic showing various Datastage Developer job openings in Virginia as of August 2026, with employment types broken down into 75% Full Time, 12% Part Time, and 13% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $118,865 per year, or $57.1 per hour.

Sr Data Engineer

Omm IT Solutions

Mclean, VA

$116K - $139K/yr

Contractor

Re-posted 5 days ago

Job description

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  • IT IS 100 % ON SITE POSITION IN Mc lean VA<\/b><\/span><\/span><\/span><\/span>
    <\/li>\n <\/ul>KEY REQUIRED SKILLS:<\/b><\/u><\/span><\/span>
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    • PySpark & Python for data pipeline development, Snowflake & AWS<\/b><\/span><\/span><\/span>
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      \n DESCRIPITION:<\/b><\/u>
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      \n We are seeking a hands\-on, delivery\-focused Senior Data Engineer to help build and scale our cloud data platform. In this role, you will design and develop modern data pipelines using PySpark, Snowflake, and AWS to optimize cloud data workloads. The ideal candidate combines strong engineering fundamentals with cloud\-native data expertise and is capable of translating complex business needs into robust, performant, and well\-documented data solutions. Experience within Fannie Mae, Freddie Mac, or equivalent GSE\/mortgage enterprise environments is highly valued.<\/span><\/span>
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      • Scalable Architecture<\/b>: Design and build scalable batch and streaming data pipelines using PySpark for large\-scale data processing.<\/span><\/span>
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      • Modernization<\/b>: Migrate legacy, on\-premises ETL workloads (e.g., IBM DataStage, Informatica) to high\-performing PySpark and Snowflake cloud pipelines.<\/span>
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      • Data Transformation<\/b>: Write production\-grade PySpark code to read from Amazon S3 (Parquet\/Delta files), execute complex transformations, and process massive datasets efficiently.
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      • Deduplication<\/b>: Design and implement robust deduplication strategies for high\-volume datasets using PySpark.
        Platform Engineering: Build and manage Snowflake warehouses, schemas, and data models optimized for enterprise analytics and business intelligence reporting.

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      • Iceberg Tables<\/b>: Design and implement Apache Iceberg tables in Snowflake to support open lakehouse architectures and data interoperability.
        Incremental Processing: Build and maintain Snowflake Dynamic Tables and Materialized Views to enable near real\-time analytics and query acceleration.

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      • PySpark Tuning<\/b>: Optimize distributed Spark jobs by leveraging partitioning, caching, broadcast joins, and shuffle optimization.
        Snowflake Optimization: Tune Snowflake workloads using clustering keys, micro\-partition pruning, query profiling, precise warehouse sizing, and strategic result caching.
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      • Cost Management:<\/b> Continuously monitor and optimize Spark jobs, Snowflake queries, and AWS infrastructure to balance speed and cloud expenditure.
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      • Data Quality:<\/b> Implement data validation, lineage tracking, and monitoring solutions across all pipeline stages to ensure high data integrity.
        Cross\-Functional Collaboration: Partner closely with data architects, business analysts, Technical Program Managers (TPMs), and corporate stakeholders to deliver dependable data products.

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      • Technical Documentation<\/b>: Author comprehensive technical designs, data schemas, and operational runbooks to ensure every pipeline is maintainable and audit\-ready.<\/span>
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        \n REQUIRED QUALIFICATION:<\/b><\/span><\/span><\/u>
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        • Experience: 6+ years of hands\-on data engineering experience in large\-scale enterprise environments.
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        • PySpark Expertise: Deep proficiency in building distributed data processing pipelines, handling S3 Parquet\/Delta files, and implementing complex transformations and deduplication logic.
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        • Snowflake Proficiency: Strong hands\-on experience with SnowSQL, Snowpipe, Streams, Tasks, and Role\-Based Access Control (RBAC). Proven track record establishing Iceberg tables, Dynamic Tables, and Materialized Views.
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        • AWS Cloud Ecosystem: Robust working knowledge of AWS services, including S3, Glue, EMR, Lambda, IAM, Step Functions, CloudWatch, and Redshift.
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        • Advanced SQL & Python: Mastery of advanced SQL techniques (window functions, CTEs, complex joins) alongside strong Python programming skills for automation, scripting, and orchestration utilities.
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        • Orchestration & Architecture: Solid understanding of data warehousing, ELT\/ETL patterns, data lakes, and lakehouse architectures using tools like Airflow or AWS Step Functions.
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        • Communication: Strong verbal and written communication skills with the ability to articulate technical decisions clearly to both technical peers and business leaders.<\/span><\/span>
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          • Industry Experience: Prior experience working within heavily regulated environments such as financial services, mortgage banking, or GSE programs (Fannie Mae \/ Freddie Mac).
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          • ETL Migration: Hands\-on experience with legacy ETL frameworks (e.g., IBM DataStage) to support modernization initiatives.
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          • DevOps & CI\/CD: Familiarity with continuous integration and continuous deployment pipelines for data infrastructure (Git, Jenkins, GitHub Actions, Terraform).
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          • Data Quality Frameworks: Exposure to automated data quality and validation frameworks (e.g., Great Expectations, dbt testing suites).
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          • Streaming Analytics: Knowledge of real\-time streaming platforms like Apache Kafka or AWS Kinesis.
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          • Professional Certifications: AWS Certified Data Analytics, AWS Certified Solutions Architect, or SnowPro Core\/Advanced certifications.
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        Apply