Overview

The Analytics Engineer is the first set of eyes on client data. They ingest, clean, and structure messy datasets, surface initial dashboards and visualizations, and prepare early data assets to support both business decision-making and downstream AI/ML experimentation. In Gigster pods, they provide the quick wins and clarity needed to set projects on the right path.

Non-Negotiables

· Proven experience ingesting and profiling data from varied sources (structured, semi-structured, unstructured).

· Fluency in SQL and Python (pandas, Jupyter) for data wrangling and exploratory analysis.

· Ability to deliver clear visualizations and dashboards (Tableau, Power BI, Looker, or equivalent).

· Experience running exploratory data analyses (EDA) to uncover patterns, anomalies, and signals.

· Strong communication skills for discovery sessions with SMEs and translating domain knowledge.

Ideal Candidate Profile

Technical Expertise

· Data Wrangling – Cleans messy, incomplete, or unstructured datasets into usable form.

· EDA – Uses statistical and visualization methods to reveal insights and anomalies.

· Visualization – Builds reference dashboards using Tableau, Power BI, Looker, matplotlib/plotly.

· Data Preparation for AI – Understands feature basics, embeddings prep, handling text/unstructured data.

· SQL + Python – Bread-and-butter toolkit for profiling, joining, and shaping data.

Soft Skills & Mindset

· Curious – Digs into data quality, completeness, and anomalies with persistence.

· Translator – Works with SMEs to align raw data with business and domain context.

· Pragmatic – Balances speed with accuracy, delivering lightweight transformations quickly.

· Communicator – Creates dashboards and visuals that provide shared reference points.

Preferred Industry & Background

· Analytics engineers, data analysts, or business intelligence professionals with hands-on SQL/Python skills.

· Engineers who have worked as the first data resource on digital transformation or AI prep projects.

· Backgrounds in consulting, SaaS, or enterprise analytics teams delivering quick wins and foundational assets.

Core Responsibilities

· Ingest and profile client data from varied sources; assess quality, completeness, and gaps.

· Run exploratory analyses (EDA) to uncover patterns, anomalies, and business-relevant signals.

· Collaborate with SMEs to align raw data with business definitions and validate assumptions.

· Produce dashboards and visuals that provide quick wins and shared context.

· Deliver cleaned, structured datasets enabling downstream AI/ML experimentation.

Traits That Stand Out

· Quickly produces dashboards that become reference points for the team.

· Anticipates downstream AI/ML needs while structuring data upfront.

· Balances hands-on technical depth with clarity in communicating findings.

· Thrives as the data-first contributor in early-stage AI pods.