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Job Description You will design, develop, and deploy AI-powered, cloud-based products. As a Data Engineer, you’ll work with large-scale, heterogeneous datasets and hybrid cloud architectures to support analytics and AI solutions. Collaborate with data scientists, infra engineers, sales specialists, and stakeholders to ensure data quality, build scalable pipelines, and optimize performance. Your work will integrate telco data with other verticals (retail, healthcare), automate DataOps/MLOps/LLMOps workflows, and deliver production-grade systems. 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. Qualifications Qualifications · Bachelor’s or Master’s in Computer Science, Software Engineering, Data Science, or equivalent experience · 4+ 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 Nice to Have · 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
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.