Data Analytics Trainer at HKBK

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 : [email protected]

Job Type: Full-time

Pay: From ₹800,000.00 per year

Benefits:

• Health insurance

Language:

• English (Preferred)

Work Location: In person