Data Analytics Trainer

3 - 8 years

9 - 11 Lacs

Posted:2 months ago| Platform: Naukri logo

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Job Type

Full Time

Job Description

JD & KRA Data Analytics Trainer

(Engineering, MBA, BCA, BBA, B.Sc./BCS cohorts)

Role Snapshot

Field

Details

Position Title

Data Analytics Trainer

Department

Upskilling & Industry Collaboration

Learner Segments

Engineering, MBA, BCA, BBA, B.Sc./BCS

Core Stack

Excel (Adv/Power Query/Power Pivot)

Primary Objectives

Build industry-ready analytics capability via project-based learning, continuous assessment, placement alignment, and measurable outcome improvements

Role Summary

Own end-to-end delivery of a multi-tool analytics curriculum covering Excel, SQL, Power BI, Tableau, Python, Applied Statistics/ML and AI assistants. Design mini/capstone projects and rigorous assignments, run continuous assessments, track each learner’s progress, and collaborate with the Placement Cell to improve tech-round conversions and overall quality.

Key Responsibilities (JD)

A. Curriculum & Delivery

  • Deliver outcome-oriented modules mapped to programme level (UG/PG/Engineering vs Management depth).
  • Excel (Foundations Advanced):

    formatting, functions, raw-data handling,

    Power Query

    cleaning & conditional columns,

    data connectors

    ,

    Power Pivot

    (cardinality, cross-filter).
  • Advanced Excel III:

    Pivot Tables, Charts, Slicers, Measures,

    Dashboarding

    .
  • MySQL:

    syntax, clauses/operators, NULL handling;

    joins, CASE, GROUP BY, HAVING

    ;

    subqueries, UNION/INTERSECT/EXCEPT, stored procedures, CTEs, window functions

    .
  • Power BI:

    connectors;

    Power Query

    (append/merge, pivot/unpivot),

    data modelling

    , DAX (measures vs calculated columns, time-intelligence, cumulative/moving average), publishing & basic RLS;

    visuals & dashboarding

    (cards/KPIs/gauges, matrices, slicers) including “ChatGPT-assisted measures” where appropriate.
  • Tableau:

    data connections; chart suite; sorting/grouping/filtering; colours/labels/tooltips;

    maps, hierarchies, actions, stories, sharing

    .
  • Maths & Applied Statistics:

    descriptive stats, probability, hypothesis testing,

    A/B testing

    .
  • Python:

    basics functions/modules, pickle,

    NumPy

    (stats on arrays),

    Pandas

    (clean/merge/concat/join),

    Matplotlib

    charts;

    web scraping

    (Requests/BeautifulSoup), table extraction, multi-page scraping, basic

    text analysis

    .
  • ML (Intro):

    regression, decision trees, random forests/ensembles; clustering, hierarchical clustering, dimensionality reduction, cross-validation, evaluation metrics/ROC.
  • AI for Analytics:

    using copilots to generate/debug code, data cleaning,

    SQL crafting

    , model-selection support, anomaly detection—

    with accuracy checks and responsible use

    .

B. Projects, Assignments & Portfolios

  • Mini projects

    per module (e.g., Excel margin bridge; SQL cohort analysis; Power BI executive dashboard; Tableau churn story; Pandas data-cleaning pipeline).
  • Capstones

    by domain (retail/fintech/ops/HR/marketing): problem framing ETL visuals insights recommendations/ROI.
  • Mandate

    portfolio publishing

    : Power BI Service/Tableau Public (or screenshots if private) + GitHub (SQL/Python, READMEs).

C. Continuous Assessment & Progress Tracking

  • Baseline diagnostic; weekly labs/quizzes; timed SQL/Excel tests; dashboard practicals.
  • Transparent

    rubrics

    (data prep, correctness, visual quality, insightfulness, documentation).
  • Maintain

    progress dashboards

    (attendance, scores, milestones, risk flags) and run

    remedial clinics

    with targeted plans.

D. Placement Enablement

  • Map teaching to

    company patterns

    (Excel/SQL tests, dashboard tasks, case interviews).
  • Conduct

    mock case interviews

    and

    storytelling drills

    ; schedule tool sprints before drives; align with roles (BA/DA/MIS/Operations Analyst).

E. Content Development & Quality

  • Build and refresh

    decks, datasets, solution keys, video snippets, quick reference guides

    ; maintain multi-difficulty question banks; quarterly updates via feedback loops.

F. Collaboration & Administration

  • Coordinate timetables/labs (licenses/connectors/data sources); ensure compliance with institutional policies and data privacy.
  • Submit

    attendance, assessment analytics, and placement-readiness reports

    on time.

G. Compliance & Ethics

  • Promote

    ethical data use

    , citation of sources, fair means in assessments, and zero-plagiarism (plagiarism checks where available).

Qualifications & Experience

  • Education:

    B.E/B.Tech/M.E./M.Tech/BSCS/MCA/M.Sc. (CS/IT/Stats) or MBA (Analytics/Operations/Marketing).
  • Experience:

    5–8+ years in analytics roles or corporate/ed-tech training with demonstrable projects and outcomes.
  • Skills:

    Advanced Excel, strong SQL, Power BI &/or Tableau, statistics for decisions, Python (desirable), data storytelling; evidence of

    project supervision

    leading to internships/placements.

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