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Ibm Data Engineer Jobs in Washington (NOW HIRING)

Omm IT Solutions

Sr Data Engineer

Mclean, VA · On-site

$116K - $139K/yr

Migrate legacy, on\-premises ETL workloads (e.g., IBM DataStage, Informatica) to high\-performing ... Experience: 6+ years of hands\-on data engineering experience in large\-scale enterprise ...

USM

IBM BPM Developer - Contract to hire

Mclean, VA · On-site

... SOA, BPM, Data Warehousing, SharePoint Consulting and IT Infrastructure. Our other offerings ... BPM Developer Location : Mclean, VA- Must relocate to work on site Contract Length: 6m+- could have ...

IBM

Federal Associate Data Scientist 2027

Herndon, VA · On-site

$60K - $61K/yr

IBM's culture of internal mobility means you'll have the freedom to explore new technologies ... Working in an Agile, collaborative environment, partnering with other scientists, engineers ...

HazeGrayCyber

Senior Data Scientist

Washington, DC · On-site

... programming, budget, and execution decisions. * Conduct predictive analysis and refine/update ... IBM Data Science Professional Certificate * Or other comparable certification programs through ...

HazeGrayCyber

Senior Data Scientist

Washington, DC · On-site

... programming, budget, and execution decisions. * Conduct predictive analysis and refine/update ... IBM Data Science Professional Certificate * Or other comparable certification programs through ...

AMERICAN SYSTEMS

Mainframe System Programmer

Mclean, VA · On-site +1

$120K - $140K/yr

HFS, ZFS, and PDS's), TSO ISPF panels, IBM's SDSF and/or equivalent ISV product, and IBM data ... Proven System programmer experience managing, maintaining, and configuring Virtual ...

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All JobsIbm Data Engineer JobsIbm Data Engineer Jobs in Washington

Ibm Data Engineer information

See Washington salary details

$50.4K

$146.9K

$201K

How much do ibm data engineer jobs pay per year?

As of Sep 9, 2026, the average yearly pay for ibm data engineer in Washington is $146,916.00, according to ZipRecruiter salary data. Most workers in this role earn between $129,700.00 and $155,700.00 per year, depending on experience, location, and employer.

What is an IBM data engineer?

An IBM Data Engineer is responsible for designing, building, and managing data pipelines and architectures to support data-driven decision-making. They work with data integration, ETL processes, cloud platforms, and big data technologies to ensure efficient data flow. IBM Data Engineers collaborate with data scientists, analysts, and business stakeholders to optimize data accessibility and performance. Their role often involves using IBM technologies such as IBM Cloud, Db2, and Watson Studio to implement scalable data solutions. Strong skills in SQL, Python, and data modeling are essential for success in this role.

What does an IBM data engineer do?

As an IBM Data Engineer, your daily responsibilities often include designing and building data pipelines, integrating data from various sources, and ensuring the reliability and quality of data architecture. You may work closely with data scientists, analysts, and business stakeholders to understand requirements and optimize data workflows. Regular tasks involve maintaining and troubleshooting ETL processes, performing data validation, and documenting solutions. Collaboration within agile teams is common, and staying updated on IBM technologies and best practices is critical for ongoing success.

What are the key skills and qualifications needed to thrive as an IBM data engineer?

To thrive as an IBM Data Engineer, you need strong proficiency in data modeling, ETL processes, and SQL or Python programming, typically supported by a bachelor’s degree in computer science, engineering, or a related field. Expertise with IBM data tools like IBM DataStage, IBM Cloud Pak for Data, and knowledge of big data platforms such as Hadoop or Spark, along with relevant IBM certifications, are highly valuable. Effective problem-solving abilities, attention to detail, and collaboration skills help you excel in cross-functional teams and adapt to evolving project needs. These skills and qualities are essential for designing robust data pipelines and delivering reliable, enterprise-level data solutions.

What are the most commonly searched types of Ibm Data Engineer jobs in Washington?

The most popular types of Ibm Data Engineer jobs in Washington are:

What are popular job titles related to Ibm Data Engineer jobs in Washington?

For Ibm Data Engineer jobs in Washington, the most frequently searched job titles are:

What job categories do people searching Ibm Data Engineer jobs in Washington look for?

The top searched job categories for Ibm Data Engineer jobs in Washington are:

What cities in Washington are hiring for Ibm Data Engineer jobs?

Cities in Washington with the most Ibm Data Engineer job openings:

Ibm Data Engineer jobs near you
Infographic showing various Ibm Data Engineer job openings in Washington as of September 2026, with employment types broken down into 66% Full Time, and 34% Contract. Highlights an 59% In-person, and 41% Remote job distribution, with an average salary of $146,916 per year, or $70.6 per hour.

Sr Data Engineer

Mclean, VA • On-site

Omm IT Solutions
See more jobs at Omm IT Solutionsexternal

$116K - $139K/yr

Contractor

Re-posted 26 days ago

Job description

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  • IT IS 100 % ON SITE POSITION IN Mc lean VA<\/b><\/span><\/span><\/span><\/span>\n
    <\/li>\n <\/ul>\n KEY REQUIRED SKILLS:<\/b><\/u><\/span><\/span>
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    • PySpark & Python for data pipeline development, Snowflake & AWS<\/b><\/span><\/span><\/span>\n
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      \n DESCRIPITION:<\/b><\/u> \n
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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>\n
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      \n RESPONSIBILITES:<\/u><\/b>\n
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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>\n
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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>\n
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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.\n
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      • Deduplication<\/b>: Design and implement robust deduplication strategies for high\-volume datasets using PySpark.\n
        Platform Engineering: Build and manage Snowflake warehouses, schemas, and data models optimized for enterprise analytics and business intelligence reporting.\n
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      • Iceberg Tables<\/b>: Design and implement Apache Iceberg tables in Snowflake to support open lakehouse architectures and data interoperability.\n
        Incremental Processing: Build and maintain Snowflake Dynamic Tables and Materialized Views to enable near real\-time analytics and query acceleration.\n
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      • PySpark Tuning<\/b>: Optimize distributed Spark jobs by leveraging partitioning, caching, broadcast joins, and shuffle optimization.\n
        Snowflake Optimization: Tune Snowflake workloads using clustering keys, micro\-partition pruning, query profiling, precise warehouse sizing, and strategic result caching.\n
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      • Cost Management:<\/b> Continuously monitor and optimize Spark jobs, Snowflake queries, and AWS infrastructure to balance speed and cloud expenditure.\n
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      • Data Quality:<\/b> Implement data validation, lineage tracking, and monitoring solutions across all pipeline stages to ensure high data integrity.\n
        Cross\-Functional Collaboration: Partner closely with data architects, business analysts, Technical Program Managers (TPMs), and corporate stakeholders to deliver dependable data products.\n
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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>\n
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        \n \n Requirements<\/h3>\n
        \n REQUIRED QUALIFICATION:<\/b><\/span><\/span><\/u>\n
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        • Experience: 6+ years of hands\-on data engineering experience in large\-scale enterprise environments.\n
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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.\n
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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.\n
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        • AWS Cloud Ecosystem: Robust working knowledge of AWS services, including S3, Glue, EMR, Lambda, IAM, Step Functions, CloudWatch, and Redshift.\n
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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.\n
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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.\n
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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>\n
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          \n PREFERRED QUALIFICATION:<\/b><\/span><\/u><\/span>\n
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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).\n
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          • ETL Migration: Hands\-on experience with legacy ETL frameworks (e.g., IBM DataStage) to support modernization initiatives.\n
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          • DevOps & CI\/CD: Familiarity with continuous integration and continuous deployment pipelines for data infrastructure (Git, Jenkins, GitHub Actions, Terraform).\n
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          • Data Quality Frameworks: Exposure to automated data quality and validation frameworks (e.g., Great Expectations, dbt testing suites).\n
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          • Streaming Analytics: Knowledge of real\-time streaming platforms like Apache Kafka or AWS Kinesis.\n
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          • Professional Certifications: AWS Certified Data Analytics, AWS Certified Solutions Architect, or SnowPro Core\/Advanced certifications.\n
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        Apply