Associate Engineer – Data Analytics

🔍 What You’ll Work On

This position sits at the intersection of data analytics and aerospace operations, giving early-career engineers exposure to real business and manufacturing data rather than limiting the work to academic datasets.

A major part of the role involves collecting, validating, standardizing, and maintaining operational and quality information across different business systems. The data may support teams working across quality, engineering, manufacturing, supply chain, and program operations.

You will also contribute to automated dashboards and KPI reports used during leadership and business reviews. This means the role is not limited to preparing datasets; the analysis needs to be translated into information that business and engineering teams can understand and use.

Trend analysis is another important responsibility. You may examine quality, manufacturing, supplier, and operational metrics to identify patterns and provide insights that support decisions around cost, quality, delivery, and productivity.

🛠 Tools & Technical Exposure

The position specifically mentions exposure to Microsoft tools and Tableau, with Power BI and Tableau identified as important business intelligence technologies candidates can learn and work with.

Microsoft Excel and PowerPoint are listed among the required capabilities. Candidates are also expected to have a basic understanding of data analytics and visualization concepts.

For candidates targeting this position, the following skills are particularly relevant:

SQL
Python
Power BI
Tableau
Microsoft Excel
PowerPoint
Data Visualization
KPI Reporting

The preferred qualifications also mention experience with SQL and Python for data analysis, dashboard development, KPI creation, and automated reporting.

📊 From Data to Business Decisions

The role provides exposure to a complete analytics workflow.

A typical assignment could involve receiving operational data, validating its quality, standardizing the information, analyzing relevant metrics, and presenting the resulting trends through dashboards or reports.

The final output may then be used by leadership or cross-functional teams to understand operational performance.

This makes communication important alongside technical ability. An analyst working with engineering and manufacturing teams needs to understand what a metric represents and communicate its significance clearly rather than simply producing charts.

Strong data visualization is useful only when the underlying information helps a team make a better operational decision.

🏭 Aerospace & Manufacturing Exposure

One of the distinctive aspects of this position is its connection to aerospace manufacturing and engineering operations.

Candidates can gain exposure to quality, manufacturing, supplier, engineering, and operational datasets while working with teams across the organization.

The position also supports Lean, Six Sigma, Kaizen, and process improvement initiatives. These methodologies are commonly associated with improving processes around cost, quality, delivery, and productivity.

For a fresher, this creates an opportunity to understand how analytics is applied outside a purely software-focused environment. The work connects technical data skills with physical manufacturing and engineering processes.

🤝 Cross-Functional Collaboration

The position requires collaboration with Quality, Engineering, Manufacturing, Supply Chain, and Program teams.

This means candidates should be comfortable working with people who may have different technical backgrounds. Some stakeholders may be interested in the underlying data, while others may primarily need a clear KPI, trend, or business insight.

Communication, willingness to collaborate, and the ability to understand business requirements are therefore important parts of the role.

RTX also describes the position as an opportunity to develop professional communication skills while working in a technology-driven organization.

🎓 Who Can Apply

The position is open to candidates with a Bachelor’s degree in Engineering in disciplines including:

• Mechanical Engineering

• Industrial Engineering

• Production Engineering

• Aerospace Engineering

• Computer Science

• Information Technology

• Electronics

• Related engineering disciplines

RTX states that the role is suitable for fresh graduates or candidates with up to two years of relevant industry experience.

Basic knowledge of data analytics and data visualization is required. Exposure to Microsoft tools or Tableau through academic projects, internships, certifications, or professional experience is also accepted.

Fresh Graduates and candidates with up to 2 years of relevant experience can be considered.

📈 What Can Strengthen Your Resume

Candidates applying for an entry-level analytics position can make their profile stronger by demonstrating practical work rather than listing tools alone.

A project involving SQL + Python + Power BI would directly align with several of the preferred qualifications. For example, a candidate could build an operations analytics project that cleans manufacturing-style data with Python, analyzes performance metrics with SQL, and presents KPIs through Power BI.

Experience with Tableau, Excel automation, KPI reporting, or process improvement projects can also strengthen the profile.

The official requirements specifically identify internships or projects involving data analytics, business intelligence, or process improvement as preferred experience.

🧠 Areas Worth Preparing

Candidates preparing for this position should be comfortable with fundamental data analytics concepts, including:

SQL: filtering, joins, aggregations, subqueries, and analytical queries.

Python: data manipulation, cleaning, basic analysis, and working with structured datasets.

Power BI / Tableau: dashboards, KPIs, filters, calculated metrics, and presenting trends clearly.

Excel: formulas, data cleaning, lookup functions, pivot tables, and reporting.

Data Quality: identifying missing, inconsistent, duplicated, or incorrectly formatted data.

Business Analytics: understanding what a KPI represents and how trends can influence operational decisions.

The preferred qualifications from RTX specifically mention SQL, Python, dashboards, KPIs, automated reports, and exposure to manufacturing, engineering, or operations data.

🤖 AI & Continuous Improvement

The role also includes exposure to AI tools for productivity, data analysis, and business reporting.

This does not replace the importance of fundamental analytics skills. Candidates should understand the underlying data and analysis before relying on AI-assisted tools.

The position also provides exposure to continuous improvement methodologies such as Lean, Six Sigma, and Kaizen, giving candidates an opportunity to understand how data can be used to identify process inefficiencies and support measurable improvements.

🌐 Work Environment

This is an onsite, full-time position in Bengaluru, based at RTX’s Collins Aerospace location in Hoodi Village, Whitefield Road.

The work involves interaction with multiple functional teams and exposure to engineering, manufacturing, quality, supply chain, and operational environments.

RTX describes the position as a technology-driven environment where candidates can develop professional communication skills and gain practical experience with data analytics in a global aerospace manufacturing setting.

🔑 Keywords for Resume

SQL • Python • Power BI • Tableau • Microsoft Excel • Microsoft PowerPoint • Data Analytics • Data Visualization • Business Intelligence • KPI Reporting • Dashboard Development • Automated Reporting • Data Cleaning • Data Validation • Data Standardization • Manufacturing Analytics • Quality Analytics • Operations Analytics • Supply Chain Analytics • Process Improvement • Lean • Six Sigma • Kaizen • AI Tools • Continuous Improvement

💡 Final Perspective

This Associate Engineer – Data Analytics position is particularly relevant for engineering graduates who want to combine data analytics with real-world manufacturing and aerospace operations. The role offers exposure to BI dashboards, KPI reporting, SQL and Python-based analysis, Microsoft tools, AI applications, and continuous improvement practices while working with cross-functional engineering and business teams.