Senior Data Engineer

We are seeking a highly skilled Senior Data Engineer to design, build, and enhance the data platforms that enable enterprise analytics, reporting, operational intelligence, and data-driven products. This role will be responsible for delivering secure, scalable, and reliable data solutions that transform complex source data into trusted, accessible information for teams across the organization., The Senior Data Engineer will collaborate closely with data architects, software engineers, analysts, data scientists, product managers, cybersecurity specialists, and business stakeholders to define requirements and deliver high-quality technical solutions. You will lead the development of modern data pipelines, data models, integration services, and data-quality capabilities across cloud and enterprise environments. The successful candidate combines strong hands-on engineering expertise with sound technical judgment, a commitment to operational excellence, and the ability to influence standards across teams. You will help shape the data architecture, improve development practices, mentor engineers, and solve complex data challenges at scale. This position offers the opportunity to create durable data capabilities that support strategic decision-making, improve customer experiences, and generate measurable business impact., * Design, develop, deploy, and maintain scalable data pipelines, data products, and integration services across enterprise platforms.

• Build batch, streaming, and near-real-time data-processing solutions to support analytical, operational, and customer-facing use cases.
• Develop and optimize data models, schemas, and storage structures for data warehouses, data lakes, and lakehouse environments.
• Partner with business and technology stakeholders to translate data requirements into secure, maintainable, and high-performing solutions.
• Integrate data from enterprise applications, APIs, cloud services, databases, files, and third-party platforms.
• Implement automated data-quality controls, validation rules, reconciliation processes, and monitoring to ensure reliable data delivery.
• Optimize data ingestion, transformation, orchestration, storage, and query performance across large and complex data sets.
• Apply data governance, metadata, lineage, retention, privacy, and access-control requirements throughout the data lifecycle.
• Establish CI/CD practices, automated testing, version control, and deployment standards for data engineering assets.
• Monitor production data services, investigate incidents, and resolve pipeline failures, data defects, and performance bottlenecks.
• Collaborate with analytics, reporting, and data science teams to deliver accessible, documented, and fit-for-purpose data sets.
• Conduct code reviews and promote reusable engineering components, technical documentation, and data platform best practices.
• Mentor data engineers and contribute to the development of technical capability and engineering standards across the organization.

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Mathematics, Engineering, or a related technical discipline; equivalent experience may be considered.
• Seven or more years of professional experience in data engineering, software engineering, analytics engineering, or a related technical field.
• Strong proficiency in SQL and at least one programming language, such as Python, Scala, Java, or Spark SQL.
• Demonstrated experience designing and supporting enterprise-scale ETL, ELT, streaming, and data-integration pipelines.
• Experience with cloud data platforms such as AWS, Microsoft Azure, Google Cloud Platform, Snowflake, Databricks, or BigQuery.
• Strong knowledge of distributed data-processing technologies, including Apache Spark, Kafka, Flink, Hadoop, or comparable frameworks.
• Experience with data orchestration and workflow-management tools such as Apache Airflow, Azure Data Factory, dbt, Prefect, or Dagster.
• Proficiency with relational and non-relational databases, including PostgreSQL, SQL Server, Oracle, MongoDB, Cassandra, or similar technologies.
• Strong understanding of data warehousing, dimensional modeling, data lakes, lakehouse architecture, and modern data platform principles.
• Experience implementing data quality, observability, cataloging, lineage, and governance capabilities.
• Familiarity with DevOps and infrastructure-as-code practices using Git, CI/CD pipelines, Docker, Kubernetes, Terraform, or similar tools.
• Knowledge of data security, privacy, encryption, identity and access management, and secure data-handling principles.
• Excellent analytical, problem-solving, communication, documentation, and stakeholder-management skills.
• Certifications such as AWS Certified Data Engineer, Microsoft Azure Data Engineer Associate, Google Professional Data Engineer, SnowPro, or Databricks certification are preferred.
• Authorization to work in the United States is required.