The Senior Data Engineer designs, builds, and maintains complex data systems for analysis and reporting, optimizing storage and processing of large data sets.
Job Purpose and Impact
The Senior Data Engineering job designs, builds and maintains complex data systems that enable data analysis and reporting. With minimal supervision, this job ensures that large sets of data are efficiently processed and made accessible for decision making.
Key Accountabilities
Qualifications
#Standard
#Standard
The Senior Data Engineering job designs, builds and maintains complex data systems that enable data analysis and reporting. With minimal supervision, this job ensures that large sets of data are efficiently processed and made accessible for decision making.
Key Accountabilities
- DATA INFRASTRUCTURE: Prepares data infrastructure to support the efficient storage and retrieval of data.
- DATA FORMATS: Examines and resolves appropriate data formats to improve data usability and accessibility across the organization.
- DATA & ANALYTICAL SOLUTIONS: Develops complex data products and solutions using advanced engineering and cloud-based technologies, ensuring they are designed and built to be scalable, sustainable and robust.
- DATA PIPELINES: Develops and maintains streaming and batch data pipelines that facilitate the seamless ingestion of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.
- DATA SYSTEMS: Reviews existing data systems and architectures to identify areas for improvement and optimization.
- STAKEHOLDER MANAGEMENT: Collaborates with multi-functional data and advanced analytic teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
- DATA FRAMEWORKS: Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.
- AUTOMATED DEPLOYMENT PIPELINES: Develops automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.
- DATA MODELING: Performs complex data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related technical field with minimum 8 years of work experience.
- CLOUD ENVIRONMENTS: Experience developing data systems on major cloud platforms (AWS, GCP, Azure).
- DATA ARCHITECTURE: Hands-on experience building modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability.
- DATA INGESTION: Demonstrated proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet).
- DATA STREAMING: Experience developing data pipelines with streaming architectures and tools (Kafka, Flink).
- DATA MODELING: Expertise in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow). Deep experience with modeling concepts like SCD and schema evolution.
- DATA TRANSFORMATION: Strong background with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.
- PROGRAMMING: Advanced programming skills in Python, Java, Scala, or similar languages. Expert-level proficiency in SQL for data manipulation and optimization.
- DEVOPS: Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies.
- DATA GOVERNANCE: Strong background in data governance principles, including data quality, privacy, and security considerations for data product development and consumption.
#Standard
#Standard
Top Skills
Airflow
AWS
Aws Glue
Azure
Dbt
Flink
GCP
Iceberg
Java
Kafka
Parquet
Python
Scala
Spark
SQL
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