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Flink Jobs in Reston, VA (NOW HIRING)

SteerBridge

Sr. Data Architect

Vienna, VA · On-site

$67.50 - $90.25/hr

Proficiency in tools like Apache Kafka, Airflow, Spark, Flink, or NiFi * Experience with cloud-based data services (AWS Glue, Google Cloud Dataflow, Azure Data Factory) * Real-time and batch data ...

Steampunk

Lead Data Architect

Mclean, VA · Remote

Mastery of distributed data processing frameworks (e.g., Spark, Databricks, Flink, Kafka, Synapse, Dataflow) and strong proficiency in SQL and Python. * Proven ability to design end-to-end ingestion ...

SteerBridge

Sr. Data Architect

Vienna, VA

$67.50 - $90.25/hr

Proficiency in tools like Apache Kafka, Airflow, Spark, Flink, or NiFi * Experience with cloud-based data services (AWS Glue, Google Cloud Dataflow, Azure Data Factory) * Real-time and batch data ...

SteerBridge

Senior Data Engineer

Vienna, VA · On-site

$106K - $144K/yr

Hands-on experience with distributed processing tools (Apache Kafka, Airflow, Spark, Flink, NiFi). * Skilled in building and orchestrating batch and real-time pipelines on cloud platforms (AWS Glue ...

SteerBridge

Senior Data Engineer

Vienna, VA

$106K - $144K/yr

Hands-on experience with distributed processing tools (Apache Kafka, Airflow, Spark, Flink, NiFi). * Skilled in building and orchestrating batch and real-time pipelines on cloud platforms (AWS Glue ...

CapTech Consulting

Lead Data Strategist

Reston, VA · On-site

Deep background with integrations and transformations of numerous types of data sources, and the solution delivery of real-time data distribution (e.g., Kafka, Kinesis, Flink), NoSQL (e.g., MongoDB ...

The Washington Post

Senior Data Engineer

Washington, DC

$120K - $163K/yr

Lead data engineering efforts using Python, Spark, Flink and other data processing technologies, delivering scalable, secure, and reliable data pipelines, and contribute to the architecture and ...

The Washington Post

Senior Data Engineer

Washington, DC · On-site

$120K - $163K/yr

Lead data engineering efforts using Python, Spark, Flink and other data processing technologies, delivering scalable, secure, and reliable data pipelines, and contribute to the architecture and ...

CapTech Consulting

Lead Data Strategist

Reston, VA · On-site

Deep background with integrations and transformations of numerous types of data sources, and the solution delivery of real-time data distribution (e.g., Kafka, Kinesis, Flink), NoSQL (e.g., MongoDB ...

ASM Research

Data Engineer Senior

Fairfax, VA · On-site

$116K - $140K/yr

Design and implement distributed data pipelines using frameworks such as Apache Spark, Kafka, or Flink to support high-volume, near-real-time and batch data processing across the enterprise.

rockITdata

Full Stack Data Engineer

Arlington, VA · On-site +1

Knowledge of streaming data processing frameworks (e.g., Apache Flink, Apache Kafka Streams). * Familiarity with data governance and security practices for protecting sensitive data. * Strong problem ...

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How much do flink jobs pay per hour?

As of Aug 7, 2026, the average hourly pay for flink in Reston, VA is $60.08, according to ZipRecruiter salary data. Most workers in this role earn between $50.53 and $70.00 per hour, depending on experience, location, and employer.

What are Flink jobs?

Flink jobs are user-defined programs that run on Apache Flink, an open-source stream processing framework. These jobs process data in real-time or in batches, allowing organizations to analyze, transform, or aggregate large volumes of data efficiently. Flink jobs are written in languages like Java, Scala, or Python and can be used for a variety of applications such as event-driven analytics, real-time monitoring, and data pipeline processing. They can be deployed on clusters to handle large-scale data processing with low latency and high throughput.

How to start a Flink job?

To start a Flink job, you need to package your application as a JAR file and submit it to a Flink cluster using the command line interface, typically with the 'flink run' command. Ensure your environment is set up with Java and Flink installed, and that your job code is properly configured for the cluster environment. Monitoring and debugging can be done through Flink's web dashboard or logs.

What is the difference between Flink vs Kafka Streams?

AspectFlinkKafka Streams
Primary UseDistributed stream processing framework for large-scale data processingClient library for real-time stream processing within Kafka
Deployment EnvironmentCluster-based, supports standalone and cloud deploymentsEmbedded within Java applications, runs on client machines
ComplexityRequires setup of cluster and infrastructureSimpler to integrate with existing Kafka setup
Use CasesComplex event processing, large-scale analyticsReal-time data transformation, lightweight processing

Flink and Kafka Streams are both popular stream processing tools, but Flink is suited for large-scale, complex processing across clusters, while Kafka Streams is ideal for lightweight, real-time processing within Kafka environments. Your choice depends on processing complexity and deployment needs.

What are some common challenges faced by Apache Flink developers and how can they be overcome?

Apache Flink developers often encounter challenges such as handling stateful stream processing at scale, ensuring low-latency data flows, and managing the complexities of distributed systems. Addressing these issues typically involves careful job design, leveraging Flink's checkpointing and state management features, and optimizing resource allocation. Collaborating closely with DevOps and data engineering teams can also help in troubleshooting deployment and performance bottlenecks, ensuring smooth operation in production environments.

What is a Flink job?

A Flink job is a program written using Apache Flink, an open-source framework for distributed stream and batch data processing. It involves defining data sources, transformations, and sinks to process large-scale data in real-time or batch mode, often requiring knowledge of Java or Scala and familiarity with Flink's APIs and environment.

What are the key skills and qualifications needed to thrive as an Apache Flink developer?

To thrive as an Apache Flink Developer, you need strong programming skills (typically in Java or Scala), a solid understanding of distributed systems, and experience with real-time data processing frameworks, preferably backed by a relevant degree in computer science or engineering. Familiarity with Flink’s APIs, stream processing concepts, and integration with tools like Kafka, Hadoop, or AWS, as well as certifications in big data technologies, are highly valuable. Analytical thinking, problem-solving abilities, and effective communication are essential soft skills for collaborating with teams and troubleshooting complex data workflows. These skills are crucial for building scalable, reliable, and efficient data pipelines that drive real-time analytics and business decisions.

What does Flink do?

A Flink job involves developing and maintaining real-time data processing applications using Apache Flink, an open-source stream processing framework. It requires skills in Java or Scala, understanding of distributed systems, and familiarity with data streaming concepts to efficiently process large-scale data in real-time environments.
What job categories do people searching Flink jobs in Reston, VA look for? The top searched job categories for Flink jobs in Reston, VA are:
What cities near Reston, VA are hiring for Flink jobs? Cities near Reston, VA with the most Flink job openings:
Flink jobs near you
Infographic showing various Flink job openings in Reston, VA as of August 2026, with employment types broken down into 56% Full Time, and 44% Contract. Highlights an 86% In-person, and 14% Remote job distribution, with an average salary of $124,976 per year, or $60.1 per hour.

Sr. Data Architect

SteerBridge

Vienna, VA • On-site

$67.50 - $90.25/hr

Full-time

Re-posted 11 days ago

Job description

SteerBridge is a modern technology company delivering innovative, mission-focused solutions to the U.S. Government and private sector. Leveraging deep expertise in federal acquisition, digital transformation, and emerging technologies, we deliver agile, commercial-grade capabilities that accelerate operational effectiveness and drive measurable mission success.
At the core of SteerBridge is our people-especially the veterans whose leadership, problem-solving mindset, and commitment to excellence elevate every project we support. We don't simply hire exceptional talent; we cultivate it, creating meaningful career pathways for veterans, military spouses, and professionals who share our passion for advancing technology and strengthening the missions we serve.
Position Overview
SteerBridge is seeking a Senior Data Architect to lead the design and evolution of enterprise-level data ecosystems. You will be responsible for architecting scalable, secure, and high-performance data infrastructures that support mission-critical aviation sustainment. This is a "player-coach" role that requires high-level strategic planning alongside hands-on engineering execution.
Key Responsibilities
Architecture & Design: Design conceptual, logical, and physical data models for complex federal environments. Lead the transition from legacy on-premises systems to modern, cloud-native (AWS/GCP) data platforms.
Pipeline Development: Architect and oversee the build of automated ETL/ELT pipelines using Python, SQL, and PySpark to ingest and transform unstructured and structured data.
Cloud Data Warehousing: Implement and optimize enterprise data warehouses using tools like AWS Redshift, Google BigQuery, AWS Glue, and Databricks.
Governance & Compliance: Establish data governance frameworks, metadata management, and data lineage in alignment with federal standards.
Performance Optimization: Conduct index/partition design, query tuning, and sharding strategies to ensure high availability and scalability for real-time analytics.
AI/ML Support: Design data architectures that facilitate AI/ML initiatives, including model training pipelines and real-time inference in production environments.
Leadership: Mentor a team of data engineers, enforce software engineering best practices (CI/CD, unit testing, documentation), and serve as a technical bridge between stakeholders and delivery teams.
Required Qualifications
  • Must be a U.S. Citizen.
  • Masters's Degree or Above in Systems Engineering, Computer Science or related field.
  • An active security clearance or the ability to obtain one is required.
  • Minimum 6+ years of experience to include:
    • Experience in data management, utilizing advanced analytics tools and platforms and Python.
    • Experience with Data Warehousing consulting/engineering or related technologies (Redshift, Databricks, BigQuery, OADW, Apache Hive, Apache Lucene).
    • Experience in scripting, tooling, and automating large-scale computing environments.
    • Extensive experience with major tools such as Python, Pandas, PySpark, NumPy, SciPy, SQL, and Git; Minor experience with TensorFlow, PyTorch, and Scikit-learn.
    • Compliance: Deep understanding of data security and federal compliance requirements.

  • Data Architecture and Design
    • Skills:
      • Data modeling (conceptual, logical, and physical)
      • Database schema design
      • Understanding of different database paradigms (relational, NoSQL, graph databases, etc.)
      • ETL (Extract, Transform, Load) processes and tools
      • Experience with modern data warehousing solutions (e.g., Redshift, Snowflake, BigQuery)
      • Understanding of dimensional modeling (star/snowflake schemas) and data vault techniques.
      • Experience designing for both OLTP and OLAP workloads.
      • Familiarity with metadata-driven design and schema evolution in data systems.
      • Experience defining data SLAs and lifecycle management policies.
      • Project Experience: Designing and implementing scalable data architectures that support business intelligence, analytics, and machine learning workflows.
  • Data Pipeline Development
    • Skills:
      • Proficiency in tools like Apache Kafka, Airflow, Spark, Flink, or NiFi
      • Experience with cloud-based data services (AWS Glue, Google Cloud Dataflow, Azure Data Factory)
      • Real-time and batch data processing
      • Automation and monitoring of data pipelines
      • Strong understanding of incremental processing, idempotency, and backfill strategies.
      • Knowledge of workflow dependency management, retries, and alerting.
      • Experience writing modular, testable, and reusable Python-based ETL code.
      • Project Experience: Leading the development of highly available, fault-tolerant, and scalable data pipelines, integrating multiple data sources, and ensuring data quality.
  • Cloud Platforms and Services
    • Skills:
      • Expertise in cloud environments (AWS, GCP, Azure)
      • Understanding of cloud-based storage (S3, Blob Storage), databases (RDS, DynamoDB), and compute resources
      • Implementing cloud-native data solutions (Data Lake, Data Warehouse, Data Mesh)
      • Experience with cost monitoring and optimization for data workloads.
      • Familiarity with hybrid and multi-cloud architectures.
      • Understanding of serverless data patterns (e.g., Lambda + S3 + Athena, Cloud Functions + BigQuery).
      • Project Experience: Migrating legacy data infrastructure to the cloud or developing new data platforms using cloud services, with a focus on cost efficiency and scalability.
  • Big Data Technologies
    • Skills:
      • Experience with big data ecosystems (Hadoop, HDFS, Hive, Spark)
      • Distributed computing, parallel processing, and handling petabyte-scale data
      • Tools for querying large datasets (Presto, Athena)
      • Understanding of lakehouse frameworks (Delta Lake, Iceberg, Hudi).
      • Familiarity with data compaction, schema evolution, and ACID guarantees in distributed storage
      • Project Experience: Building and managing big data platforms to enable large-scale analytics, often incorporating structured and unstructured data.
  • Database Administration and Optimization
    • Skills:
      • Expertise in database technologies (SQL, NoSQL, GraphDBs)
      • Query optimization, indexing, and partitioning strategies
      • Backup, replication, and disaster recovery planning
      • Understanding of query execution plans, cost-based optimization, and caching strategies.
      • Experience performing index and partition design based on query patterns.
      • Familiarity with data versioning and temporal tables.
      • Experience profiling and optimizing application code interacting with databases.
      • Project Experience: Performance tuning for complex queries, implementing database replication and sharding strategies to support high availability and scalability.
  • Data Governance and Security
    • Skills:
      • Data privacy, encryption, and compliance with regulations (GDPR, CCPA)
      • Implementing data governance frameworks (data lineage, cataloging, metadata management)
      • Role-based access control and user management for sensitive data
      • Experience with automated policy enforcement and data lineage visualization tools (e.g., DataHub, Collibra, Alation).
      • Knowledge of data quality frameworks integrated into CI/CD pipelines.
      • Familiarity with data contract testing between producer and consumer teams.
      • Project Experience: Developing and implementing data governance policies and security controls across the organization's data assets, ensuring compliance with industry standards.
  • Programming and Scripting Languages
    • Skills:
      • Proficiency in Python and SQL
      • Experience with version control (Git) and CI/CD for data engineering (Gitlab, Jenkins, CircleCI)
      • API design and integration (Postman)
      • Strong understanding of object-oriented programming (OOP) principles and design patterns in Python.
      • Familiarity with software engineering best practices (modularity, testing, documentation, linting).
      • Understanding of algorithmic complexity (Big O notation) and ability to optimize code for scale.
      • Experience with parallel and distributed computation frameworks (Spark, Dask, Ray).
      • Ability to profile and debug performance bottlenecks in data workflows.
      • Use of type hinting, logging frameworks, and automated testing frameworks (pytest, unittest)
  • AI/ML Pipeline Support and Analytics
    • Skills:
      • Experience in supporting data scientists with feature engineering, data wrangling, and model deployment
      • Knowledge of ML orchestration tools (MLflow, Kubeflow)
      • Hands-on experience with analytics tools (e.g., Tableau, Power BI)
      • Familiarity with feature store design and model feature lineage tracking.
      • Understanding of data versioning and reproducibility for ML workflows.
      • Experience supporting real-time model inference pipelines.
      • Project Experience: Designing architectures that support AI/ML initiatives, enabling scalable data pipelines for training models, and supporting experimentation in the production environment.
  • Leadership and Mentorship
    • Skills:
      • Leading data engineering teams, cross-functional collaboration with data scientists, analysts, and business units
      • Project management (Agile, Scrum, Kanban) and stakeholder communication
      • Experience with mentorship and growing junior data engineers
      • Experience establishing data architecture standards and best practices.
      • Ability to review and approve technical designs for consistency and scalability.
      • Proven success in mentoring engineers in code quality, modeling, and system design.
      • Project Experience: Leading the technical direction for large-scale data initiatives, such as enterprise data lake implementations or the creation of a unified data platform.

$155,000 - $180,000 a year
Annually, commensurate with experience and location.
If you would like information about how your application is processed, please contact us.

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