AI Talent · Data Engineering & Data Science Hiring

    Hire Data Engineering & Data Science Talent in India

    Data engineers, analytics and data platform engineers, streaming engineers, data architects, data and applied scientists, decision scientists and AI data engineers, each assessed on the systems and decisions they have owned.

    Discuss a Data Hire Which role do we need?

    In brief

    Data engineering and data science hiring at NeoIntelli covers data engineers, analytics engineers, data platform and streaming engineers, data architects, data scientists, applied and decision scientists, and AI data engineers who build retrieval, feature and evaluation infrastructure in India. Specialist recruiters source, NeoHireX screens and runs first-round AI interviews, and practicing data engineers and scientists validate ownership of real pipelines, platforms, analyses and models before you see a shortlist.

    Answer first

    Do we need a Data Scientist, ML Engineer or Data Engineer?

    Start from the constraint you actually have. The title follows.

    Which data or ML role to hire first
    Your situationHire firstWhy
    You have a business question and messy dataData ScientistAnalysis and experimentation come first; production modelling can wait.
    You have a model idea but no reliable dataData EngineerNo model survives an unreliable pipeline. Build the foundation first.
    You have a validated model that must run in productionML EngineerProductionization, inference and monitoring are engineering work.
    You are building RAG or agents on enterprise documentsAI Data Engineer, then GenAI EngineerRetrieval quality is a data problem before it is a model problem.

    Role profiles

    What each role owns, and how we tell ownership from exposure.

    Data engineering

    Engineers who build and run the platform and pipelines every model, dashboard and agent depends on.

    Data Engineer

    Owns: Reliable batch and streaming pipelines, data modelling, quality checks, orchestration and the operational care of data products other teams depend on.

    Production signals we look for

    • Pipelines they own that page them when they break, and what they changed
    • Data contracts or quality checks they introduced
    • Cost or performance improvements with numbers
    • Modelling decisions explained for the consumers they served

    Resume signals that mislead

    • SQL-only analysts labelled as data engineers
    • Tool lists (Spark, Airflow, dbt) with no owned system
    • ETL maintenance with no design ownership

    How NeoIntelli evaluates

    • End-to-end walkthrough of one pipeline: source, transformation, quality, consumers
    • Modelling and incremental-load design exercise
    • Debugging scenario for a late or wrong dataset

    Analytics Engineer

    Owns: Transformed, tested, documented datasets and metrics that analysts, data scientists and AI features consume, with software practices applied to analytics code.

    Production signals we look for

    • A metrics layer or model set with tests and documentation
    • Refactors that removed duplicate definitions
    • Close working relationships with analysts and product

    Resume signals that mislead

    • Dashboard builders with no transformation ownership
    • dbt familiarity without testing or CI

    How NeoIntelli evaluates

    • Modelling exercise with conflicting metric definitions
    • Testing and documentation practices review
    • Stakeholder scenario on a disputed number

    Data Platform Engineer

    Owns: The lakehouse or warehouse platform: ingestion frameworks, storage, compute, orchestration, catalog, access control, cost and the developer experience of the teams that build on it.

    Production signals we look for

    • Platform capabilities adopted by multiple teams
    • Access control, residency and cost handled as design inputs
    • Migrations or upgrades they led without breaking consumers

    Resume signals that mislead

    • Cloud certifications without a platform they ran
    • Single-team pipeline work described as platform

    How NeoIntelli evaluates

    • Platform design exercise with multi-team, governance and cost constraints
    • Operations scenario: outage, cost spike, access request
    • Developer-experience discussion

    Streaming Engineer

    Owns: Event-driven and real-time data systems: Kafka or equivalent, stream processing, schema evolution, exactly-once semantics where required, replay and monitoring.

    Production signals we look for

    • Streaming systems with stated latency and delivery guarantees
    • Schema evolution and replay handled in production
    • Backpressure or partitioning problems they solved

    Resume signals that mislead

    • Batch engineers with a Kafka connector on the resume
    • No experience with failure modes of streaming systems

    How NeoIntelli evaluates

    • Design exercise for a real-time feature with delivery guarantees
    • Failure-mode discussion: late data, duplicates, reprocessing
    • Monitoring approach review

    Data Architect

    Owns: Data architecture across domains: platform choices, modelling standards, integration patterns, governance and lineage, and the guidance of engineers implementing them.

    Production signals we look for

    • Architectures adopted across teams with the trade-offs explicit
    • Governance and lineage designed in, not retrofitted
    • Decisions they changed after production evidence

    Resume signals that mislead

    • Vendor reference architectures as experience
    • Architecture without accountability for what was built

    How NeoIntelli evaluates

    • Architecture review of a multi-domain scenario
    • Governance and residency design questions
    • Technical leadership assessment

    Data science

    Data Scientist / Applied Scientist

    Owns: Analysis, experimentation and models that change decisions or products, with statistical rigor and clear communication of uncertainty. Applied scientists lean toward productionized models.

    Production signals we look for

    • A decision or product change with their analysis behind it
    • Experiments designed with power, controls and caveats
    • Models handed to engineering with a production plan

    Resume signals that mislead

    • Dashboards or reporting described as data science
    • Modelling with no reasoning about the statistics

    How NeoIntelli evaluates

    • Case exercise from question to recommendation
    • Statistics and experimentation fundamentals
    • Communication of uncertainty to a stakeholder

    Decision Scientist / Advanced Analytics

    Owns: Quantitative decision support: causal analysis, forecasting, optimization and experimentation tied to specific business levers.

    Production signals we look for

    • Causal or forecasting work that a business owner acted on
    • Optimization models used in operations
    • Understanding of the limits of their methods

    Resume signals that mislead

    • Descriptive analytics presented as decision science
    • Methods listed without a decision attached

    How NeoIntelli evaluates

    • Case on a real business lever with messy data
    • Methods discussion with failure modes
    • Stakeholder communication assessment

    AI data

    Data roles that exist because of AI workloads: retrieval infrastructure, AI-ready data, feature infrastructure and evaluation datasets.

    AI Data Engineer

    Owns: Vector and retrieval infrastructure, document ingestion and parsing for RAG, AI-ready data products, feature infrastructure and versioned evaluation datasets.

    Production signals we look for

    • Retrieval indexes they built and kept fresh and permission-aware
    • Evaluation datasets treated as owned data products
    • Feature or embedding pipelines in production

    Resume signals that mislead

    • Vector database tutorials as experience
    • Data engineers with no exposure to AI consumers

    How NeoIntelli evaluates

    • Design exercise for a permission-aware retrieval data pipeline
    • Evaluation dataset ownership discussion
    • Freshness and cost scenario

    We interview. You hire.

    The validation flow behind every data shortlist.

    1. STEP 01

      Role calibration

      Understand what this person actually has to build, own and operate, and at what seniority.

    2. STEP 02

      Specialist sourcing

      Search the AI, data and platform talent pools relevant to the role, not a generic database.

    3. STEP 03

      NeoHireX screening

      Role-calibrated screening and ranking on NeoIntelli's own Hiring OS.

    4. STEP 04

      AI first-round interview

      Structured candidate evaluation before a human hour is spent.

    5. STEP 05

      Senior technical round

      Practicing AI and data engineers assess technical depth against the role.

    6. STEP 06

      Expert validation

      Validate ownership, architecture decisions and production experience behind the resume.

    7. STEP 07

      Qualified shortlist

      You see evaluation context and evidence, not another stack of CVs.

    8. STEP 08

      You decide

      Your team runs the final interviews and makes the hiring decision. NeoHireX never does.

    The foundation these engineers build for an AI GCC is described under Data Engineering for AI GCCs.

    Buyer questions

    Questions about data engineering and data science recruitment in India.

    Do we need a Data Scientist, ML Engineer or Data Engineer?

    Start from the constraint. A business question with messy data needs a data scientist. A model idea with unreliable data needs a data engineer first. A validated model that must run in production needs an ML engineer. Most first teams need a data engineer before either of the others.

    How do you evaluate a data engineer?

    On a pipeline they own: source, transformation, quality checks, consumers and what happens when it fails. Practicing data engineers run the technical round with a modelling exercise and a debugging scenario, and the evidence is recorded with the shortlist.

    Is an analytics engineer a data engineer?

    Adjacent, not identical. An analytics engineer owns transformed, tested datasets and metrics close to the business. A data engineer owns ingestion, pipelines and platform reliability. Small teams sometimes combine them; the role calibration decides which one you need.

    What is an AI data engineer?

    A data engineer whose consumers are retrieval systems, models and agents: vector and retrieval infrastructure, document ingestion, AI-ready data products, feature infrastructure and evaluation datasets. The role is new enough that most candidates grew into it from data or backend engineering.

    How does data hiring connect to building an AI GCC?

    Data engineering is the first capability most AI GCCs need. Data Engineering for AI GCCs describes the foundation the team will build; the AI Micro GCC model runs the operating environment around them.

    Discuss a data hire.

    Tell us what the data has to feed: dashboards, models, retrieval or agents. We will calibrate the role to that, show you the evaluation plan and start sourcing.

    Discuss a Data Hire Explore Data Engineering for AI GCCs

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