Staff Data Engineer
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Job Summary:
We are seeking a highly experienced Staff Data Engineer to lead the design, development, and optimisation of our data architecture, pipelines, and workflows. This role will serve as a technical lead within the organisation, setting best practices, mentoring team members, and solving complex data challenges to enable data-driven decision-making at scale.
As a Staff Data Engineer, you will collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to design systems that transform raw data into actionable insights while ensuring scalability, security, and reliability.
Key Responsibilities:
Technical Leadership
Design, and implement scalable and reliable data pipelines, ensuring the processing of large volumes of structured and unstructured data.
Define and enforce data engineering best practices, coding standards, and architectural principles across teams.
Conduct code reviews and provide mentorship to junior and senior data engineers.
Data Pipeline Development
Build and maintain batch and real-time data pipelines using tools such as Apache Spark, Kinesis, and AWS services.
Works with multiple teams to coordinate the event-driven architecture, managing inter-dependencies and promoting consistency.
Ensure data quality, governance, and security by implementing monitoring, validation, and compliance tools.
Collaboration & Cross-Functional Engagement
Partner with product, analytics, and data science teams to understand business requirements and translate them into technical solutions.
Work closely with DevOps and software engineering teams to deploy and maintain production-ready data infrastructure.
Innovation & Scalability
Evaluate and recommend emerging technologies and frameworks to ensure the data platform remains future-proof.
Drive initiatives to improve the performance, scalability, and efficiency of existing systems.
Required Skills & Experience
12+ years of experience in data engineering field, with at least 2 years in a senior or staff-level role.
Expertise in designing and implementing scalable data architectures for big data platforms.
Strong programming skills in Python, Scala.
Deep experience with distributed data processing systems such as Apache Spark, Databricks, Delta Lake.
Proficiency with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (Dynamo).
Strong understanding of ETL/ELT workflows, data warehousing concepts, and modern data lake architectures.
Employ the established Data Governance model to sustain Data Quality for the data objects and implement the necessary operating mechanisms to ensure compliance
Knowledge of CI/CD practices.
Excellent problem-solving skills and the ability to design creative, efficient solutions for complex data challenges.
Background in AI, machine learning pipelines is a plus
Proactive, self-driven, and detail-oriented with a strong sense of ownership.
Staff Data Engineer
To see similar active jobs please follow this link: Remote Development jobs
Job Summary:
We are seeking a highly experienced Staff Data Engineer to lead the design, development, and optimisation of our data architecture, pipelines, and workflows. This role will serve as a technical lead within the organisation, setting best practices, mentoring team members, and solving complex data challenges to enable data-driven decision-making at scale.
As a Staff Data Engineer, you will collaborate with cross-functional teams, including data scientists, analysts, and software engineers, to design systems that transform raw data into actionable insights while ensuring scalability, security, and reliability.
Key Responsibilities:
Technical Leadership
Design, and implement scalable and reliable data pipelines, ensuring the processing of large volumes of structured and unstructured data.
Define and enforce data engineering best practices, coding standards, and architectural principles across teams.
Conduct code reviews and provide mentorship to junior and senior data engineers.
Data Pipeline Development
Build and maintain batch and real-time data pipelines using tools such as Apache Spark, Kinesis, and AWS services.
Works with multiple teams to coordinate the event-driven architecture, managing inter-dependencies and promoting consistency.
Ensure data quality, governance, and security by implementing monitoring, validation, and compliance tools.
Collaboration & Cross-Functional Engagement
Partner with product, analytics, and data science teams to understand business requirements and translate them into technical solutions.
Work closely with DevOps and software engineering teams to deploy and maintain production-ready data infrastructure.
Innovation & Scalability
Evaluate and recommend emerging technologies and frameworks to ensure the data platform remains future-proof.
Drive initiatives to improve the performance, scalability, and efficiency of existing systems.
Required Skills & Experience
12+ years of experience in data engineering field, with at least 2 years in a senior or staff-level role.
Expertise in designing and implementing scalable data architectures for big data platforms.
Strong programming skills in Python, Scala.
Deep experience with distributed data processing systems such as Apache Spark, Databricks, Delta Lake.
Proficiency with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (Dynamo).
Strong understanding of ETL/ELT workflows, data warehousing concepts, and modern data lake architectures.
Employ the established Data Governance model to sustain Data Quality for the data objects and implement the necessary operating mechanisms to ensure compliance
Knowledge of CI/CD practices.
Excellent problem-solving skills and the ability to design creative, efficient solutions for complex data challenges.
Background in AI, machine learning pipelines is a plus
Proactive, self-driven, and detail-oriented with a strong sense of ownership.