Time since we first saw it
Day 17
Similar jobs usually come down between day 6 and day 56.
From 13,630 similar postings that came down. Coming down is not always a hire.
Data Engineer
Noida, Uttar Pradesh, India
Live, first seen by us 17 days ago- Pay
- Pay not listed
- Work setting
On-site — work setting source
Work setup in the employer’s posting: On-site
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: Full time
From the employer’s posting
What you’ll work on
Full postingDesign and deliver data pipelines and platforms end to end, including production support
Design dimensional and Medallion (Bronze/Silver/Gold) data models
Build and maintain ETL/ELT pipelines using Python, SQL, PySpark and Azure Data Factory
Build batch and incremental/CDC loads and tune Spark and SQL performance and cost
Gather requirements and agree acceptance criteria with client stakeholders
From the employer’s posting
Own the design and delivery of data pipelines and platforms end to end: requirements, architecture, build, testing, deployment and production support.
Design data models (dimensional, SCD, Medallion Bronze/Silver/Gold) that match how the business actually uses the data.
Build and maintain scalable ETL/ELT pipelines using Python, SQL, PySpark and Azure Data Factory on Azure Databricks or Microsoft Fabric.
Implement batch, incremental/CDC and streaming loads, and tune Spark and SQL performance and cost.
Work directly with client stakeholders to gather requirements, explain design choices and agree acceptance criteria.
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 27, 202617 days ago
- Recorded sightings
- 17
- Last seen by us
- Nov 13, 2026
- Reposted
- once since Jun 3, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications. We count a repost when the same title and location comes back as a new listing within 60 days of the last one coming down. Some hiring systems expire listings on a schedule, so repost rates vary by system.
How this job compares
ScaleUp Ally is hiring
1 open job
Open jobs by the week we first saw them, last 12 weeks.
- 100% of open jobs were reposted
See how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- SQL
- Azure
- Databricks
- Delta Lake
- Kafka
- Power BI
- Spark
- Terraform
- PySpark
Where the posting names them
Purpose of Job We are looking for a Senior Data Engineer who is equally strong on the data side (modelling, data quality, SQL, understanding the business) and the engineering side (Python, PySpark, CI/CD, performance, reliability). You will own the design and delivery of production data platforms from requirements to production support, work directly with clients, and share your knowledge with the wider engineering team. Your stack will include Python, SQL, PySpark, Azure Databricks or Microsoft Fabric, Azure Data Factory, Delta Lake, and Lakehouse and Data Warehouse designs. Essential Qualifications Bachelor's degree in any discipline Information Systems, or a related field. Holds at least 5-8 years in data engineering Skills ADF l Microsoft Fabric l SQL l ETL/ELT l Power BI | Python Duties Own the design and delivery of data pipelines and platforms end to end: requirements, architecture, build, testing, deployment and production support. Design data models (dimensional, SCD, Medallion Bronze/Silver/Gold) that match how the business actually uses the data. Build and maintain scalable ETL/ELT pipelines using Python, SQL, PySpark and Azure Data Factory on Azure Databricks or Microsoft Fabric. Implement batch, incremental/CDC and streaming loads, and tune Spark and SQL performance and cost. Build data quality checks, monitoring and alerting into every pipeline, and apply governance, lineage and access controls. Automate testing and deployment with Git and CI/CD, and follow engineering best practices in code reviews. Work directly with client stakeholders to gather requirements, explain design choices and agree acceptance criteria. Integrate data from APIs, transactional systems and external platforms. Investigate and resolve complex production issues and document root causes. Document your solutions and share knowledge with the team, including peer code reviews. Requirements Required Qualifications Data side: strong SQL, dimensional and Medallion/Lakehouse modelling, data quality practices, and the ability to understand business requirements. Engineering side: strong Python and PySpark, Git and CI/CD, automated testing, and performance tuning of high-volume workloads. Hands-on experience with Azure Databricks or Microsoft Fabric, and Azure Data Factory or an equivalent orchestration tool. Proven delivery of batch and incremental/CDC pipelines on Delta Lake or similar. Client-facing experience: requirement gathering, stakeholder communication and clear technical documentation. Measurable impact you can explain, such as runtime, cost or data quality improvements. Good to Have Streaming experience (Spark Structured Streaming, Kafka). Governance and lineage tools (Unity Catalog, Azure Purview). Infrastructure as code (Terraform). Power BI and semantic-layer knowledge, to work effectively with BI teams. Microsoft certifications such as DP-203 or DP-600. Benefits Benefits and Additional Information Working hours: 10:00 AM – 7:00 PM Working days: 5 days a week (plus 1st & 3rd Saturdays working) Medical Insurance coverage for employees Provident Fund (PF) facility Quarterly parties and yearly outings/trips for team bonding Regular check-ins with leadership for growth and feedback Recognition awards to celebrate high performance Fun activities and team engagement sessions throughout the year
Your next step
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Demand for Data Engineer roles
New Data Engineer postings at the employers we follow are up 7% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under Data Engineer, by the work the posting describes.
Data engineers build and run the pipelines that copy data out of apps and outside services into a warehouse or lake, and fix them when a source changes or a load fails.
Expect SQL, Python, a cloud platform, a scheduler such as Airflow, and often tools for large data such as Spark or Kafka.
An analytics engineer works one step later, turning the raw data a data engineer lands into clean tables people can query.
US government figures for database architects
- Median yearly pay, May 2025
- $139,500
- Expected change, 2025 to 2035
- 9.4% more jobsagainst 3.5% for all jobs
The government has no code for data engineers, so these figures are for the closest group, database architects, which includes data integration and data warehousing specialists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1243.
Job description
Purpose of Job We are looking for a Senior Data Engineer who is equally strong on the data side (modelling, data quality, SQL, understanding the business) and the engineering side (Python, PySpark, CI/CD, performance, reliability). You will own the design and delivery of production data platforms from requirements to production support, work directly with clients, and share your knowledge with the wider engineering team. Your stack will include Python, SQL, PySpark, Azure Databricks or Microsoft Fabric, Azure Data Factory, Delta Lake, and Lakehouse and Data Warehouse designs. Essential Qualifications Bachelor's degree in any discipline Information Systems, or a related field. Holds at least 5-8 years in data engineering Skills ADF l Microsoft Fabric l SQL l ETL/ELT l Power BI | Python Duties Own the design and delivery of data pipelines and platforms end to end: requirements, architecture, build, testing, deployment and production support. Design data models (dimensional, SCD, Medallion Bronze/Silver/Gold) that match how the business actually uses the data. Build and maintain scalable ETL/ELT pipelines using Python, SQL, PySpark and Azure Data Factory on Azure Databricks or Microsoft Fabric. Implement batch, incremental/CDC and streaming loads, and tune Spark and SQL performance and cost. Build data quality checks, monitoring and alerting into every pipeline, and apply governance, lineage and access controls. Automate testing and deployment with Git and CI/CD, and follow engineering best practices in code reviews. Work directly with client stakeholders to gather requirements, explain design choices and agree acceptance criteria. Integrate data from APIs, transactional systems and external platforms. Investigate and resolve complex production issues and document root causes. Document your solutions and share knowledge with the team, including peer code reviews. Requirements Required Qualifications Data side: strong SQL, dimensional and Medallion/Lakehouse modelling, data quality practices, and the ability to understand business requirements. Engineering side: strong Python and PySpark, Git and CI/CD, automated testing, and performance tuning of high-volume workloads. Hands-on experience with Azure Databricks or Microsoft Fabric, and Azure Data Factory or an equivalent orchestration tool. Proven delivery of batch and incremental/CDC pipelines on Delta Lake or similar. Client-facing experience: requirement gathering, stakeholder communication and clear technical documentation. Measurable impact you can explain, such as runtime, cost or data quality improvements. Good to Have Streaming experience (Spark Structured Streaming, Kafka). Governance and lineage tools (Unity Catalog, Azure Purview). Infrastructure as code (Terraform). Power BI and semantic-layer knowledge, to work effectively with BI teams. Microsoft certifications such as DP-203 or DP-600. Benefits Benefits and Additional Information Working hours: 10:00 AM – 7:00 PM Working days: 5 days a week (plus 1st & 3rd Saturdays working) Medical Insurance coverage for employees Provident Fund (PF) facility Quarterly parties and yearly outings/trips for team bonding Regular check-ins with leadership for growth and feedback Recognition awards to celebrate high performance Fun activities and team engagement sessions throughout the year
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- Pay
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- Location & working pattern
Noida, Uttar Pradesh, India
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