Coditation is hiring a Data Engineer in Pune to join its Data Science team and build reliable, scalable data pipelines that support analytics and machine learning initiatives. This opportunity is well suited for professionals with 2–4 years of experience who have strong Python, SQL, ETL/ELT and AWS skills and want to develop deeper expertise in modern data engineering.
📍 Location: Pune, Maharashtra, India
💼 Role: Data Engineer
🧑💻 Experience: 2–4 Years
🎯 Team: Data Science
☁️ Primary Cloud: AWS
📩 Apply: samiksha@coditation.com
🏢 About Coditation
Coditation is a technology engineering company focused on areas including AI & Machine Learning, Data Engineering, DevOps/SRE, application modernization and digital engineering. Its current data-engineering practice focuses on building production-grade data platforms, governed ingestion, real-time pipelines, data-quality controls and AI-ready enterprise data layers.
Coditation states that it has more than 12 years of experience building data platforms at enterprise scale and more than 80 production lakehouse and warehouse engagements. Its data-engineering capabilities include enterprise data platforms, analytical infrastructure, data quality and AI-readiness.
The company’s careers page currently lists a Technical Data Engineer position for 2–4 years of experience in Pune, alongside roles in AI, Python, backend engineering, data science and other technologies.
💻 Why This Role Is Good for Your Experience
This is a strong opportunity for a 2–4 year professional because the JD covers the fundamental building blocks of modern data engineering rather than focusing only on one ETL tool.
You will work around Python, SQL, ETL/ELT, structured and semi-structured data, Pandas, NumPy and AWS services such as S3, Redshift and Glue. These technologies provide a practical foundation for moving toward Data Engineer, Cloud Data Engineer or Analytics Engineering roles.
Key career benefits include:
- 🐍 Stronger Python development for data workloads
- 🗄️ Advanced SQL and data manipulation
- 🔄 Real-world ETL/ELT pipeline development
- ☁️ AWS cloud data engineering exposure
- 📦 Structured and semi-structured data processing
- 📊 Data quality and pipeline monitoring
- 🤖 Exposure to analytics and ML-supporting data infrastructure
🧠 Skills Required
Must-have: Python, SQL, ETL/ELT, Data Pipelines, Pandas, NumPy, Data Cleaning, Data Transformation, Structured Data, Semi-Structured Data and Data Quality.
AWS: S3, Redshift and Glue should be your highest-priority cloud services.
Additional skills: PostgreSQL/MySQL, REST APIs, JSON, CSV, Parquet, Git, Linux, AWS IAM fundamentals, CloudWatch, data warehousing, dimensional modeling and basic CI/CD.
For a stronger profile, understand batch versus streaming pipelines, partitioning, incremental loads, schema evolution, idempotency and pipeline failure recovery.
🎯 Expected Coditation Interview Rounds
Round 1 – Initial/HR Screening: Experience, Python/SQL exposure, projects, notice period, Pune availability and compensation expectations.
Round 2 – Technical Data Engineering: SQL queries, Python coding, ETL concepts, data structures, Pandas/NumPy and data-quality scenarios.
Round 3 – AWS/Data Pipeline Discussion: S3, Redshift, Glue, pipeline architecture, incremental processing, monitoring and troubleshooting.
Round 4 – Project/Managerial Discussion: Your previous data projects, ownership, problem-solving, communication and ability to work with Data Science teams.
💰 Expected Salary Range
Public salary data for Coditation Systems in Pune lists Data Engineer compensation around ₹9–20 LPA, based on five reported salaries. (Glassdoor)
For this particular 2–4 year Data Engineer position, a realistic expectation would be approximately ₹8–14 LPA, with stronger candidates possessing hands-on AWS, pipeline architecture and data-quality experience potentially targeting the upper end.
🔥 Preparation Tips – Specific to This JD
Don’t prepare AWS as a list of service definitions. Prepare by designing an actual pipeline.
Imagine you receive daily customer transaction files in CSV and JSON format in S3. Explain how you would use AWS Glue to ingest and transform the data, load curated datasets into Redshift, validate data quality and monitor pipeline failures.
Be prepared to discuss:
- How would you handle duplicate records?
- How would you implement incremental loading?
- What happens when the schema changes?
- How would you validate completeness and accuracy?
- How would you identify a failed or delayed pipeline?
- How would you optimize a slow SQL transformation?
For Python, practice Pandas joins, groupby, aggregation, missing-value handling, data type conversion and efficient processing. For SQL, prioritize joins, CTEs, window functions, subqueries, aggregations and query optimization.
📄 Resume Tips for This Data Engineer Role
Your resume should immediately show Python + SQL + ETL + AWS rather than presenting yourself simply as a “Data Analyst.”
Use a headline such as:
Data Engineer | Python | SQL | ETL/ELT | AWS | S3 | Redshift | Glue | Data Pipelines
- “Developed and maintained scalable ETL pipelines using Python and SQL for analytics workloads.”
- “Built AWS-based ingestion workflows using S3 and Glue for structured and semi-structured datasets.”
- “Optimized SQL transformations, reducing pipeline execution time by X%.”
- “Implemented data-quality checks for completeness, duplicates and schema consistency.”
- “Created monitoring mechanisms to identify pipeline failures and data freshness issues.”
If you have worked with CSV, JSON, Parquet, APIs or large datasets, explicitly mention them. Also quantify data volume, processing time and performance improvements wherever possible.
📩 How to Apply
📩samiksha@coditation.com
Interested candidates should share their updated profile with samiksha@coditation.com.
📌 Suggested subject:
Application – Data Engineer | 2–4 Years | Pune
Attach your latest resume and make sure your Python, SQL, ETL/ELT and AWS experience is clearly visible. If you have GitHub projects, data-pipeline projects or a portfolio demonstrating AWS/data engineering work, include the portfolio link.
🚀 Final Takeaway
This Coditation opportunity is a good match for 2–4 year Data Engineers who want practical exposure to cloud data engineering, ETL/ELT, Python, SQL and data-quality engineering. The AWS stack of S3 + Glue + Redshift, combined with support for analytics and ML initiatives, gives the role a useful foundation for progressing toward modern cloud data engineering.
If you’re applying, don’t just list AWS services on your resume—show what you built with them, how much data you processed and how you improved pipeline reliability or performance. 🎯
