Data Analytics Trainer

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.