Coforge is hiring experienced Lead/Senior Data Engineers with strong expertise in DBT, SQL, Python, PySpark, Data Modeling, ETL/ELT, and Cloud, with GCP preferred. The opportunity is available across Greater Noida, Pune, and Hyderabad and is well suited for professionals with 5โ8 years of data engineering experience who want to work on scalable data platforms and modern analytics ecosystems.
๐ข About Coforge
Coforge is a global AI-native engineering services company focused on AI-led engineering, data, cloud, digital transformation, and industry-specific technology solutions. Coforge currently describes its scale as approximately 45,000 engineers, designers, and industry experts, with operations across 33 countries and 54 global delivery centers. Its capabilities include Data, Cloud, Engineering, Quality Engineering, and AI-led business process solutions.
๐ Job Details
๐น Company: Coforge
๐น Position: Lead Data Engineer / Senior Data Engineer
๐น Experience: 5โ8 years
๐น Locations: Greater Noida, Pune, Hyderabad
๐น Core Skills: DBT, Python, PySpark, SQL, Data Modeling
๐น Cloud: GCP preferred; AWS/Azure experience may also be relevant
๐น Key GCP Services: BigQuery, Dataflow, Dataproc, Cloud Composer, GCS
๐น Primary Focus: Data Engineering, ETL/ELT, Transformation, Data Quality & Cloud Data Platforms
๐ Why This Role Is Good for Your Experience
The combination of DBT + advanced SQL + Python/PySpark + cloud + dimensional modeling is especially valuable because these are core components of modern analytics engineering and cloud data platforms. Experience with BigQuery, Dataflow, Dataproc, and Cloud Composer can also strengthen your profile for future Senior Data Engineer, Lead Data Engineer, Data Platform Engineer, Analytics Engineer, or Data Architect opportunities.
๐งฐ Skills Required
๐น Advanced SQL and query optimization
๐น DBT models, tests, macros, sources, snapshots, and incremental models
๐น Python for data processing and automation
๐น PySpark and distributed-data processing
๐น Star Schema and Snowflake Schema
๐น Dimensional data modeling and warehouse design
๐น ETL/ELT pipeline development
๐น Data cleansing, reconciliation, validation, and quality checks
๐น BigQuery and Google Cloud Storage
๐น Dataflow, Dataproc, and Cloud Composer
๐น AWS or Azure equivalent cloud services
๐น Git and CI/CD
๐น Data governance, metadata, and documentation
๐น Pipeline monitoring and troubleshooting
๐น Performance optimization and cost optimization
๐น Understanding of partitioning and clustering
๐น API/file/database-based data ingestion
๐น Strong communication and stakeholder-management skills
๐ฏ Expected Coforge Interview Rounds
Round 1 โ Recruiter/Initial Screening: Expect discussion around your experience, current project, notice period, location preference, CTC, expected compensation, and technology stack.
Round 2 โ Data Engineering Technical Round: Prepare for SQL, Python, PySpark, ETL/ELT, data modeling, DBT, warehouse concepts, pipeline architecture, and troubleshooting.
Round 3 โ Advanced Technical/Managerial Round: Expect architecture discussions, optimization, data quality, cloud services, production issues, mentoring, Agile/Scrum, and stakeholder management. Recent Coforge data-engineering interview reports specifically mention SQL, Spark, OOPs, coding, production issues, client communication, and Scrum-related questions.
Round 4 โ Client/Project Discussion: For client-facing positions, be ready to explain your architecture decisions, business requirements, production support experience, and ability to communicate technical solutions clearly.
Round 5 โ HR: Compensation, joining date, work location, career goals, documentation, and other employment-related discussions.
๐ฐ Expected Salary Range
Coforge does not publish an official salary range for this particular opening. Current market data provides a useful reference: Glassdoor’s recent Coforge data shows Senior Data Engineer base pay around โน10โ16.4 LPA, with an average of approximately โน15 LPA. However, individual reported salaries are significantly higher in some cases, including โน18โ21 LPA for 4โ6 years in Hyderabad, โน19 LPA in Greater Noida, and โน37โ43 LPA for 7โ9 years in Gurgaon.
For this 5โ8-year role requiring DBT, PySpark, advanced SQL, data modeling, and GCP, a practical indicative expectation is โน15โ25+ LPA, depending on skill depth, location, client/project, current CTC, internal level, and negotiation. This is a market estimate, not an official Coforge compensation range.
๐ง Preparation Tips
๐ฅ Make DBT your priority: Prepare models, sources, seeds, tests, snapshots, macros, incremental models, ref(), source(), materializations, documentation, and DBT project structure.
๐๏ธ Master advanced SQL: Practice complex joins, CTEs, window functions, ranking, deduplication, slowly changing dimensions, aggregations, and query optimization.
โก Prepare a PySpark pipeline: Be able to explain how you would process a large dataset, handle partitions, avoid unnecessary shuffles, optimize joins, and manage failures.
โ๏ธ Know GCP architecture: Understand how BigQuery, GCS, Dataflow, Dataproc, and Cloud Composer can work together in an end-to-end pipeline.
๐ Practice data modeling: Given raw transactional data, design a Star Schema and explain why you selected particular facts, dimensions, keys, grain, and relationships.
๐งช Focus on data quality: Prepare scenarios involving duplicate records, missing data, schema changes, reconciliation failures, late-arriving data, and incorrect aggregates.
๐ฐ Understand cloud cost optimization: Prepare examples involving BigQuery partitioning/clustering, reducing scanned data, efficient transformations, and avoiding unnecessary compute.
๐จ Prepare production scenarios: Explain how you would troubleshoot a failed pipeline, identify the root cause, recover data safely, and prevent recurrence.
๐ Resume Tips
โ
Put DBT, SQL, Python, PySpark, Data Modeling, and GCP near the top of your technical skills.
โ
Quantify pipeline improvements such as processing-time reduction, cost reduction, or increased data throughput.
โ
Mention the number/scale of pipelines, datasets, tables, or data volumes you handled where possible.
โ
Describe actual DBT work using models, tests, macros, snapshots, or incremental processing.
โ
Highlight BigQuery optimization, partitioning, clustering, and query-performance improvements if applicable.
โ
Include Star/Snowflake Schema experience and explain the business purpose of your models.
โ
Mention CI/CD, Git, orchestration, monitoring, and data-quality implementation.
โ
Add mentoring or technical-leadership achievements because this is a Lead-level opportunity.
โ
Avoid generic statements like โworked on data pipelinesโ; instead write measurable achievements such as โBuilt DBT-based ELT pipelines processing X million records daily and reduced transformation runtime by X%.โ
๐ How to Apply
Interested candidates can apply by sharing their updated profile with Shriya Jain at Coforge. Before applying, make sure your resume clearly reflects the required combination of DBT + Python + PySpark + SQL + Data Modeling + Cloud/GCP.
๐ฉ Apply via Email: shriya.jain@coforge.com
๐ Final Verdict
This Coforge opportunity is an excellent fit for experienced Data Engineers who want to strengthen their profile around DBT, advanced SQL, PySpark, cloud data engineering, and modern data modeling. The strongest candidates will not only know individual tools but will be able to design an end-to-end solution: ingest data, transform it with DBT, model it for analytics, validate quality, optimize cloud workloads, automate deployment, monitor pipelines, and troubleshoot production failures.
If you have 5โ8 years of experience and can demonstrate hands-on work across DBT, SQL, Python/PySpark and cloud platformsโespecially GCPโthis role is worth targeting.
