Overview
[Job Overview]
Full job description
[Job Overview] We are looking for an experienced Senior Data Engineer to join our engineering team and play a key role in building and scaling our enterprise data platform. You will design, develop, and maintain high-quality data warehouses and data-driven applications that power analytics, reconciliation, and business decision-making across the organization. This role requires strong expertise in modern data architectures, pipeline engineering, and data quality management. The ideal candidate combines hands-on technical capability with a deep commitment to reliability, scalability, and governance in a regulated environment. [Responsibilities] · Data operations: own day-to-day operations of data platforms/pipelines capacity, stability, upgrades, deployments, and recovery drills to sustain high availability and low latency. · Data collection: design/manage multi-source ingestion (exchanges, internal and external systems), protocol parsing, and robust retry mechanisms. · Develop rule-based and statistical data quality checks (completeness, uniqueness, time alignment, anomaly detection, error handling). · Implement automated remediation, reconciliation workflows, and historical backfilling. · Establish monitoring and alerting frameworks to ensure trusted, production-grade datasets. · End-to-End pipelines: plan and maintain scalable ETL/ELT including scheduling, caching, partitioning, modelling, schema evolution, and lineage to support both batch and real-time streaming. · Enforce data access controls, encryption, auditing, and classification to comply with internal policies and external regulatory requirements (including PII management). · Apply Infrastructure-as-Code, data versioning, data tests, and CI/CD to improve predictability and reduce manual risk. · Contribute to embedded GenAI and LLM-powered data applications for enterprise analytics, reconciliation, and internal productivity use cases. · Partner with analytics and product teams to operationalize AI-driven data solutions. [Requirements] · Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field. · 5+ years of experience in data engineering, data platform architecture, or AI/ML engineering. · Strong experience with modern cloud data platforms (e.g., Snowflake, Databricks, BigQuery, Redshift). · Hands-on experience building BI data foundations and supporting GenAI / LLM architectures. · Proficiency in SQL and workflow orchestration tools (e.g., Airflow), streaming platforms (e.g., Kafka), and pipeline design best practices. · Solid understanding of data warehouse development lifecycles and dimensional modeling concepts. · Familiarity with GitLab and CI/CD pipelines. · Strong debugging, performance tuning, and problem-solving skills. · Working knowledge of data governance, lineage, privacy, and security frameworks.
Tips for this job
Practical Job and Scholarship guidance. These tips do not replace official rules or create new eligibility requirements.
- Tailor the CV and application to the responsibilities and required skills stated on the official employer page.
- Use concrete evidence of relevant work, projects and measurable results rather than generic claims.
- Confirm location, work authorization, remote restrictions and sponsorship terms before applying.
- Apply through the original employer or official recruitment destination shown on this page.
Verification notes
laptop-ats-crawler v2
Job and Scholarship is the discovery and verification layer. Confirm eligibility, dates, salary/funding and application instructions on the original source before submitting anything.
Aift Careers ↗Browse current Job and Scholarship listings from Aift Careers →
