Senior Data Engineer | Data Integration Developer
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Introduction
Centurion | Gauteng | Permanent
7+ years experience required - Senior
Hands on Engineer | Data Integration Developer
This role is for someone that is not afraid to get their hands dirty.
Hands-on role that owns the full data engineering lifecycle:
- from ingestion and transformation through to platform architecture and production deployment – designing, building and running complex data pipelines on AWS.
Technical Skills Required:
- Python (advanced) – data pipeline development, automation and scripting.
- SQL (advanced) – complex query writing, performance optimisation and stored procedures.
- AWS (mid–advanced) – S3, Glue, Lambda, Redshift, RDS, IAM, CloudWatch, Athena.
- Data warehousing and data modelling – star/snowflake schemas, dimensional and analytics-oriented design.
- ETL/ELT pipeline development – batch, incremental and near-real-time patterns.
- Data lakehouse architecture on cloud-native platforms.
- CI/CD and Git – version control, automated testing and deployment pipelines.
- Power BI – supporting BI teams, data model integration and semantic layer understanding.
- Experience ingesting data from relational databases (e.g. SQL Server, Oracle), cloud storage, APIs, SFTP, MongoDB and file-based sources.
Highly Valued (Differentiating Skills):
- Apache Spark / PySpark for large-scale distributed processing.
- dbt for analytics engineering and transformation layer management.
- Apache Airflow or AWS Step Functions for orchestration.
- Terraform or CloudFormation (infrastructure-as-code).
- Real-time streaming – Kafka, Kinesis or similar.
- Machine learning workflow support – feature engineering and model data pipelines.
- Microsoft Azure or GCP as secondary cloud exposure.
Senior Data Engineer, AWS, Python, SQL, Data Engineering, ETL/ELT, Data Lakehouse, Data Warehousing, Power BI, CI/CD, Agile, AI-enabled engineering, Financial Services, Centurion, Hybrid
Duties & Responsibilities
Delivery Ownership within Agile Execution
- Contribute to data engineering outcomes aligned to sprint goals and programme-level commitments.
- Take part in Agile ceremonies – planning, estimation, prioritisation and continuous improvement.
- Decompose data features into implementable tasks with reliable effort estimates.
- Ensure outputs meet agreed functional, performance and data quality expectations.
Data Engineering & Pipeline Development
- Design and implement scalable ingestion and transformation pipelines across multiple systems and data domains, supporting batch and incremental processing.
- Build in error handling, monitoring and alerting to keep pipelines robust.
- Implement data validation and reconciliation mechanisms to maintain confidence in data assets.
Platform Design & Architecture (AWS)
- Design solutions aligned to the data platform strategy and target architecture, and contribute to the evolution of the cloud-based data lakehouse environment.
- Assess the impact of design choices on security, performance, cost and supportability.
- Identify integration points, upstream/downstream dependencies and risks early in the delivery lifecycle.
Technical Leadership, Coaching & Standards
- Provide guidance and technical oversight to less experienced data engineers.
- Support analytics, BI and data science teams with clarity on data structures, availability and pipeline behaviour.
- Review code and configuration, and help define shared standards, patterns and best practices.
- Optimise and simplify existing pipelines and data models.
AI-Enabled Engineering (Governance-Led)
- Promote responsible use of AI to improve engineering productivity and solution quality, within group and business-unit governance frameworks.
- Use only enterprise-approved AI tooling, and ensure no sensitive, proprietary or client information is exposed to public or unapproved AI platforms.
- Validate all AI-assisted outputs and retain accountability for correctness, compliance and production readiness.
- Apply AI to understand data flows and lineage, improve pipeline logic, support query formulation and test scenario creation, and accelerate documentation.
DevOps, Operational Support & Incident Response
- Apply version control, automated testing, CI/CD and structured releases.
- Contribute to operational readiness, deployment pipelines and monitoring.
- Diagnose and resolve data incidents, perform root cause analysis and implement corrective actions.
Security, Risk, Compliance & Governance
- Build solutions with privacy, access control and regulatory considerations embedded by design.
- Identify and mitigate data risks, and support remediation of audit findings and compliance gaps.
- Maintain accurate documentation of pipelines, data models and operational considerations, and keep work tracking up to date in Jira.
- Adhere to delivery governance, change management and release processes.
Stakeholder Engagement
- Communicate technical progress, constraints and decisions clearly to business and technical stakeholders.
- Collaborate across technology and analytics teams and contribute positively to team culture.
Desired Experience & Qualification
Skills & Experience:
- 7+ years’ professional experience in data engineering or related roles.
- Demonstrated delivery of production-ready, end-to-end data pipelines and cloud data platforms.
- Strong exposure to AWS-based data architectures.
- Experience across the full stack – ingestion, transformation, modelling and serving – not just a single layer.
- Hands-on experience working within Agile or SAFe delivery environments.
- Experience in financial services, insurance, investment management or other regulated data environments (preferred).
- Practical experience applying AI tooling in engineering workflows – including using AI responsibly and validating its outputs – to support pipeline understanding, optimisation, testing or documentation.
- Experience coaching or guiding other engineers.
Interested?
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