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)

,

SQL (MySQL)

,

Power BI (DAX/Modeling)

,

Tableau

,

Python (NumPy, Pandas, Matplotlib)

,

Applied Statistics & ML (intro)

,

AI-assisted analytics

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.

Key Result Areas (KRA) with KPIs, Targets & Weightage

KRA

KPI / Measurement

Target (Per Semester unless stated)

Weight

Training Delivery & Coverage

Syllabus completion; planned vs delivered hours; average attendance

? 95%

coverage;

? 80%

attendance

10%

Excel Proficiency

Practical labs; dashboard build quality

? 75%

learners score

? 70%

+

1

Excel dashboard

8%

SQL Proficiency (MySQL)

Timed query test (JOINs, window, CTE)

? 70%

learners score

? 70%

12%

Power BI Competency

Modelling, DAX measures, published report

100%

publish

1 PBIX

(star schema +

? 6

measures)

10%

Tableau Competency

Dashboard with actions, map, story

100%

publish

1

story with actions

6%

Python & Data Wrangling

Pandas cleaning task; Matplotlib EDA

? 70%

complete wrangling +

4-chart

EDA

Applied Statistics & ML

A/B or ML case--method & interpretation

? 70%

score

? 70%

Project Delivery (Mini & Capstone)

On-time submissions; rubric scores; reproducibility

100%

submit;

? 75%

teams score

? 70/100

Placement Readiness & Conversion

Internal screen pass; company-pattern readiness; outcomes

? 75%

clear internal screen;

yearly

:

+10%

conversion uplift

Portfolio & Publication

Power BI/Tableau artifacts; GitHub (SQL/Python)

? 70%

learners with

2+

public artifacts & repo

Student Satisfaction (QoS)

Module/semester feedback

Avg rating

? 4.3/5

Content Refresh & Innovation

New cases/datasets; updated decks/job-aids

1

new capstone + 10 new problems/semester (or quarterly refresh)

Reporting & Stakeholder Collaboration

Timely analytics to Dept/Placement; industry talks

100%

reports on time;

? 2

industry sessions/semester

Apply Through email :

careers1@hkbk.edu.in

Job Type: Full-time

Pay: From ?800,000.00 per year

Benefits:

Health insurance
Language:

English (Preferred)
Work Location: In person