Data Engineering at ScaleupAlly

Noida, Uttar Pradesh, India -

Full Time

Start Date

Immediate

Expiry Date

17 Apr, 26

Salary

0.0

Posted On

17 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Azure Data Factory, Microsoft Fabric, Python, ETL, ELT, SQL, Data Warehouse, Lakehouse, Data Quality, Performance Optimization, CI/CD, Data Governance, Mentoring, Problem Solving, Communication

Industry

IT Services and IT Consulting

Description

Data Engineering & Architecture Design, develop, and maintain scalable, high-performance data pipelines Work extensively with Azure Data Factory and Microsoft Fabric Build robust ETL/ELT frameworks using Python Design and optimize Lakehouse / Data Warehouse architectures Handle large-scale datasets efficiently (high volume and throughput) Write and optimize complex SQL queries for performance and reliability Integrate data from multiple sources including APIs, transactional systems, and external platforms Leadership & Delivery (Hands-on) Lead and mentor a team of data engineers while remaining actively involved in coding and solution design Perform hands-on development for critical pipelines, complex transformations, and performance optimisation. Conduct code reviews and enforce best practices, design patterns, and coding standards Act as the technical owner for data engineering deliverables Quality, Performance & Reliability Implement data quality checks, validations, and monitoring Optimize pipelines for performance, scalability, and cost Ensure reliability, fault tolerance, and error handling in production systems Follow data security, access control, and compliance best practices Lead troubleshooting, root-cause analysis, and production issue resolution Collaboration & Continuous Improvement Work closely with BI, analytics, product, and business teams Translate business requirements into scalable technical solutions Stay up to date with modern data engineering tools, technologies, and techniques Proactively suggest architectural and process improvement Requirements 3-6 years of experience in the Data Engineering field. Strong hands-on experience in Python for data engineering, including building and maintaining production-grade, large-scale data pipelines Advanced experience with Azure Data Factory and Azure-based data platforms for orchestration, integration, and scalable data processing Working experience with Microsoft Fabric, including Lakehouse and data engineering workloads, along with a strong understanding of ETL/ELT and data warehousing concepts Expert-level SQL skills covering complex query development, optimization, indexing, and partitioning for high-performance systems Proven experience handling large-volume, high-throughput data and distributed processing environment. Experience with analytics and visualization platforms such as Power BI Knowledge of Delta Lake, Spark, and distributed data processing frameworks Experience implementing CI/CD practices for data pipelines and data engineering workflows Exposure to data governance, lineage, metadata management, and compliance-driven environments such as fintech or high-transaction systems Hands-on leadership mindset with strong ownership and accountability for outcomes Ability to mentor, guide, and grow junior engineers while leading by example Clear and effective communication with technical and non-technical stakeholders Strong problem-solving, analytical reasoning, and decision-making skills Benefits 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

Responsibilities

Design, develop, and maintain scalable data pipelines while leading a team of data engineers. Ensure data quality, performance, and reliability in production systems.