Role Overview

We are looking for a Senior Data Engineer with strong hands-on experience in building, optimizing, and operating large-scale, production-grade data platforms. This role focuses on designing reliable and scalable data pipelines, enabling analytics and business insights, and ensuring high standards of data quality, performance, security, and availability across cloud-based environments (Azure preferred; multi-cloud exposure is a plus). The candidate will collaborate closely with product, analytics, and business teams to deliver scalable, governed, and high-impact data solutions.

Key Responsibilities

• Design, build, and maintain scalable ETL/ELT pipelines for analytics, reporting, and downstream applications.
• Develop and optimize complex SQL queries (joins, window functions, query tuning) across platforms such as BigQuery and MS SQL Server.
• Build robust Python-based data engineering solutions, including ingestion from APIs/files, testing, and reusable modular packaging.
• Orchestrate workflows using Apache Airflow, including DAG design, scheduling, retries, dependencies, SLAs, alerts, and secrets management.
• Develop and optimize data processing using Databricks on Azure (Apache Spark / PySpark) with performance and cost efficiency.
• Implement data modeling and warehousing solutions (dimensional models, incremental loads, CDC patterns, Delta Lake).
• Ensure data quality, reliability, monitoring, and lineage, including validation checks and observability dashboards.
• Design and manage databases across RDBMS and NoSQL, with hands-on experience in SQL Server and MongoDB.
• Apply Git and CI/CD best practices (branching strategies, PR reviews, automated deployments, release quality controls).
• Implement enterprise-grade data security and governance, including access controls and compliance-aligned practices.
• Provide technical leadership, mentor junior engineers, and contribute to data engineering standards and best practices.

Required Skills & Experience 

• 10+ years of experience in data engineering or related roles.
• Strong expertise in SQL, Python, Apache Airflow, Spark, and cloud-based data platforms (Azure preferred).
• Solid understanding of distributed systems, data warehousing, and performance optimization techniques.
• Proven track record of delivering production-grade, highly reliable data solutions in enterprise environments.
• Strong problem-solving ability and ability to work across cross-functional business and technical teams. 

Preferred: 

• Experience with Azure Data Lake (ADLS Gen2), Azure Data Factory (ADF), Synapse Analytics, Key Vault, Purview.
• Experience in multi-cloud or mixed stack environments (e.g., BigQuery / GCP in addition to Azure).
• Familiarity with data observability tools, metadata/catalog tooling, and governance frameworks.
• Exposure to streaming/event-driven data pipelines (Kafka/EventHub) is a plus. 

Equal Opportunity Statement  

• We value diversity and inclusion. As an equal opportunity employer, we welcome applicants from all backgrounds and walks of life.  

Security & Confidentiality Statement  

• This role may involve access to sensitive business information. The selected candidate is expected to follow internal information security policies and maintain strict confidentiality when handling proprietary or confidential data.