We are hiring: Spark Developer – 06-August-2026 US Remote

Hiring Organization: SparkStream Dynamics

Client ID: 847QMTR

Job ID: 932LKPV

Role Overview

SparkStream Dynamics is seeking a precise, technically proficient, and highly motivated Spark Developer to join our remote-first big data engineering team. In this full-time role, you will be responsible for designing, building, and optimizing distributed data processing applications using Apache Spark. We value high-performance coding, efficient memory management, and robust data pipeline architecture. Whether you are an experienced developer or a talented fresher looking for a supportive environment to launch your career in large-scale data systems, we offer a mentorship-rich culture where your contributions will directly impact our real-time data processing capabilities.

Key Responsibilities

• Develop and maintain high-speed, scalable data processing jobs using Apache Spark (PySpark or Scala).
• Design and optimize complex data pipelines for both batch and real-time streaming (Spark Streaming).
• Fine-tune Spark job performance, including task parallelism, memory management, and shuffle operations.
• Build and manage data integrations between diverse storage sources (e.g., S3, Delta Lake, Snowflake, Kafka).
• Debug and resolve complex performance bottlenecks and processing failures in distributed environments.
• Collaborate with data scientists and infrastructure teams to support intensive computational workloads.
• Participate in peer code reviews and architectural planning to maintain engineering excellence.
• Contribute to the continuous improvement of internal CI/CD pipelines and deployment automation.
• Research and implement new features and optimizations within the Spark and big data ecosystems.
• Document data transformations, job configurations, and technical workflows for long-term reliability.

Required Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
• Strong proficiency in Python or Scala with extensive experience in Apache Spark.
• Solid understanding of distributed systems, functional programming, and parallel computing.
• Experience in building and managing ETL/ELT pipelines.
• Familiarity with version control systems like Git.
• Excellent communication skills and the ability to work effectively in a remote, distributed team.
• A proactive, problem-solving mindset and a passion for large-scale data engineering.
• Freshers with strong academic foundations in distributed systems or data projects are encouraged to apply; we provide extensive mentorship.
• Reliable high-speed internet connection for consistent, professional virtual collaboration.

Preferred Skills

• Experience with cloud-native Spark platforms (Databricks, AWS EMR, GCP Dataproc).
• Knowledge of data formats (Parquet, Avro, Delta Lake).
• Understanding of SQL and relational database concepts.
• Experience with messaging systems like Kafka.
• Familiarity with containerization (Docker, Kubernetes) for application deployment.

What We Offer

• Competitive Annual Salary: $212,450.
• 100% remote flexibility from any location in the United States.
• Comprehensive health, dental, and vision insurance packages.
• Retirement savings plan with company matching contributions.
• Generous Paid Time Off (PTO), including holidays and personal wellness days.
• Structured career development, mentorship, and support for professional certifications.
• A highly collaborative, inclusive, and mission-driven engineering culture.

Employment Details

Workplace Type: Remote

Location: United States (US)

Job Type: Full-Time

Employment Type: FULL_TIME

Employer Address: 700 SparkWay, Suite 400, Chicago, IL 60601

Freshers Can Apply: Yes

Standard Application Notice

SparkStream Dynamics is an equal opportunity employer. We value diversity and are committed to creating an inclusive culture for all employees. All hiring decisions are based on merit, qualifications, and the business needs of the organization. Please submit your application using the form below.