# Data Intelligence Specialist Internship at CryptoMize Apply now for the Data Intelligence Specialist Internship in Delhi at CryptoMize — a 6-month certified program on a rolling, always-hiring intake. Data Intelligence Specialists here work on the data layer that powers the agency’s intelligence platforms (CLAIRVOYANCE CX, PERCEPTION X2): the datasets, the indexes, the scoring models, the versioned feature stores. Interns do not shadow from the sidelines: from the first month you read the platform data model, write the SQL and Python that build and validate it, and contribute to the analytics that move client decisions. The role is the same operational model as every other CryptoMize cell — capacity is hired ahead of the next engagement, not after — and the work that ships under your name in the program is the work that decides the next offer. If you are a fresher from a quantitative discipline who wants data engineering experience that is genuinely production-grade, this is the internship description, the required skills, the eligibility, and the selection process, in full. ## Facts - Location: New Delhi (HQ), CDR Building, Chhatarpur - Type: 6-month internship, certification provided - Availability: immediate, rolling intake (always hiring) - Compensation: discussed at screening ## What you will actually do - Read the production data model of the intelligence platforms (CLAIRVOYANCE CX, PERCEPTION X2) and the analytics that run on it — schema, indexes, the daily feature-store updates, the validation jobs that gate the data going to dashboards - Build the data-pipeline scripts and dbt-style models that produce the daily feature-store snapshot the analytics cell runs against — the work that defines what every Data Analyst, Data Scientist, and Strategist at CryptoMize sees in their morning read - Tune the SQL and Python that produce the per-engagement baselines — the metrics that ground every client-facing report and the internal review - Author the validation jobs that gate data going downstream — anomaly detection, drift checks, the kinds of things that catch data poisoning before it reaches a client - Run the data-quality forensics for live engagements: when a client reports a discrepancy, you are the first person to find the cause in the data layer - Document the data layer — schema diagrams, lineage, the kind of documentation that lets an analyst onboard without tribal knowledge - Carry a scoped platform-data mini-project in the final months — a measured improvement to a pipeline or a validation job, documented to client-reportable standard ## Skills - Advanced SQL — production-grade schema, indexes, query plans, performance on multi-GB tables - Python for data engineering — pandas, PyArrow, dbt-class transformation frameworks, and the discipline of test coverage on data jobs - Airflow / dbt / Dagster-class workflow orchestration — DAGs, sensors, the kinds of things that turn ad-hoc scripts into a reliable platform - Data modeling — star, snowflake, slowly-changing-dimensions, the modeling decisions that make the difference between a pipeline that survives a year and one that needs constant repair - Data-quality engineering — anomaly detection, drift checks, expectation tests, the things that gate the data going downstream - Data lineage and documentation — schema diagrams, the discipline of writing what you know so the next person does not have to guess - Postgres internals — query plans, indexes, vacuum, the operational details that distinguish a working pipeline from a flaky one - Backfills and reversibility — every change to a production schema is reversible, with a recorded rollback path - NDA-grade discretion with client data — non-negotiable - Communication with analysts and engineers — the data layer is a shared product, and the work ships on a feedback loop with the people who consume it - Calm under incident pressure — when the dashboards are wrong, the analysis is the first place to look, and the answer is often simple once you find it - Intellectual honesty — reporting the query, not the answer you wanted ## Tools - PostgreSQL 15 (production-grade schema, query plans, indexes) - Python 3.11 (pandas, PyArrow, dbt-class frameworks) - dbt (data build tool, models, tests, lineage) - Airflow / Dagster (workflow orchestration) - Git (data-jobs version control, review discipline) ## Questions and answers ### What does a Data Intelligence Specialist intern do at CryptoMize? A Data Intelligence Specialist intern works on the data layer that powers the agency’s intelligence platforms — the datasets, the indexes, the scoring models, and the validation jobs that gate the data going to dashboards. The work is platform-grade production engineering, not analytics homework. ### What is the eligibility for the Data Intelligence Specialist Internship? Final-year students or recent graduates in computer science, data engineering, or related quantitative disciplines. Demonstrated SQL and Python skill, with the discipline to write code other people can read. ### Is the Data Intelligence Specialist Internship paid? Compensation is discussed during screening and depends on demonstrated skill, engagement classification, urgency, and track record. We deliberately do not publish figures — every intern is evaluated individually. ### How long is the internship, and is a certificate provided? The program runs 6 months at our New Delhi HQ, with a certificate provided on completion — and, more importantly, a portfolio of real platform-data work. Strong interns receive full-time offers. ### What is the selection process for the Data Intelligence Specialist Internship? A short application form, a SQL-and-Python screening task on a realistic data-pipeline brief, and a final interview with the data lead where you walk through your task reasoning. The whole cycle typically completes within two weeks because intake is rolling and the data cell is staffed ahead of client demand. Apply: https://cryptomize.com/careers/internship/apply/