As a Data Engineer, you will:
Ensure Data Quality & Consistency
Validate, clean, and standardize data (e.g., geolocation attributes) to maintain integrity.
Define and implement data quality metrics (completeness, uniqueness, accuracy) with automated checks and reporting.
Build & Maintain Data Pipelines
Develop ETL/ELT workflows (PySpark, Airflow) to ingest, transform, and load data into warehouses (S3, Postgres, Redshift, MongoDB).
Automate DataOps/MLOps/LLMOps pipelines with CI/CD (Airflow, GitLab CI/CD, Jenkins), including model training, deployment, and monitoring.
Design Data Models & Schemas
Translate requirements into normalized/denormalized structures, star/snowflake schemas, or data vaults.
Optimize storage (tables, indexes, partitions, materialized views, columnar encodings) and tune queries (sort/distribution keys, vacuum).
Integrate & Enrich Telco Data
Map 4G/5G infrastructure metadata to geospatial context, augment 5G metrics with legacy 4G, and create unified time-series datasets.
Consume analytics/ML endpoints and real-time streams (Kafka, Kinesis), designing aggregated-data APIs with proper versioning (Swagger/OpenAPI).
Manage Cloud Infrastructure
Provision and configure resources (AWS S3, EMR, Redshift, RDS) using IaC (Terraform, CloudFormation), ensuring security (IAM, VPC, encryption).
Monitor performance (CloudWatch, Prometheus, Grafana), define SLAs for data freshness and system uptime, and automate backups/DR processes.
Collaborate Cross-Functionally & Document
Clarify objectives with data owners, data scientists, and stakeholders; partner with infra and security teams to maintain compliance (PDPA, GDPR).
Document schemas, ETL procedures, and runbooks; enforce version control and mentor junior engineers on best practices.
Requirements
- Degree in Computer Science, Software Engineering, Data Science, or equivalent experience
- 2+ years in data engineering, analytics, or related AI/ML role
- Proficient in Python for ETL/data engineering and Spark (PySpark) for large-scale pipelines
- Experience with Big Data frameworks and SQL engines (Spark SQL, Redshift, PostgreSQL) for data marts and analytics
- Hands-on with Airflow (or equivalent) to orchestrate ETL workflows and GitLab CI/CD or Jenkins for pipeline automation
- Familiar with relational (PostgreSQL, Redshift) and NoSQL (MongoDB) stores: data modeling, indexing, partitioning, and schema evolution
- Proven ability to implement scalable storage solutions: tables, indexes, partitions, materialized views, columnar encodings
- Skilled in query optimization: execution plans, sort/distribution keys, vacuum maintenance, and cost-optimization strategies (cluster resizing, Spectrum)
- Experience with cloud platforms (AWS): S3/EMR/Glue, Redshift and containerization (Docker, Kubernetes)
- Infrastructure as Code using Terraform or CloudFormation for provisioning and drift detection
- Knowledge of MLOps/LLMOps: auto-scaling ML systems, model registry management, and CI/CD for model deployment
- Strong problem-solving, attention to detail, and the ability to collaborate with cross-functional teams
- Exposure to serverless architectures (AWS Lambda) for event-driven pipelines
- Familiarity with vector databases, data mesh, or lakehouse architectures
- Experience using BI/visualization tools (Tableau, QuickSight, Grafana) for data quality dashboards
- Hands-on with data quality frameworks (Deequ) or LLM-based data applications (NL-- >SQL generation)
- Participation in GenAI POCs (RAG pipelines, Agentic AI demos, geomobility analytics)
- Client-facing or stakeholder-management experience in data-driven/AI projects