Sr. Data Developer to design, implement, and optimize robust data systems across software portfolio's

Our client is looking for a Sr. Data Developer to design, implement, and optimize robust data systems across software portfolio's.

Overview:

Data Projects -This role focuses on advancing cloud-based and industrial IoT (IIoT) data architectures to enable real-time analytics, seamless system integration, and insightful business intelligence. You will collaborate across cross-functional teams in a hybrid work environment, ensuring scalable, reliable, and high-performance data infrastructure

Must Have's:

• 5+ years of professional experience in data engineering, database development, or data application development
• Proficient in SQL, DQL, DMQL, or equivalent query languages
• Experience programming languages (e.g., Python, Java, Scala)
• Experience with modern data platforms such as Snowflake, Redshift, BigQuery
• Experience with cloud environments (e.g., Azure, AWS)
• Working knowledge of source control, ticketing, and collaboration tools (e.g., Git, GitLab, Jira, Confluence)
• Experience working with relational databases such as PostgreSQL, MySQL, SQL Server
• Bachelor’s degree or higher in Computer Science, Computer Engineering, Data Engineering, or a related field; or equivalent hands-on experience.

Nice to Have's:

· Experience with real-time or near-real-time data processing tools (e.g., Apache Kafka, Spark Streaming, Flink)

· Familiarity with containerization and orchestration (Docker, Kubernetes)

· Knowledge of NoSQL databases (e.g., MongoDB, Cassandra)

· Contributions to open-source projects or participation in data communities

· Familiarity with Agile, Scrum, or Lean software development methodologies

Responsibilities:

· Design, build, and maintain scalable data pipelines and ETL frameworks to support enterprise-wide data initiatives.

· Develop and implement data management solutions across cloud and on-premises IIoT environments.

· Collaborate with software engineers, system architects, and business stakeholders to define data architecture and integration requirements.

· Research and integrate modern data technologies (e.g., lakehouse, streaming, edge computing) to improve feature sets, reduce operational costs, and enhance service quality.

· Enforce data governance, security, validation, and monitoring protocols to maintain high data quality and system integrity.

· Incorporate DevOps principles and CI/CD practices into data engineering workflows.

· Embrace a culture of inclusion, innovation, and continuous improvement.