Sr. Data Engineering Lead, Onsite, VP

Who We Are Looking For

ONSITE:
Due to the role requirements this job needs to be performed primarily in the office with some flex work opportunities available.

As a Data Engineer Lead, you will be responsible for developing, maintaining, and optimizing data pipelines and systems that support the acquisition, storage, transformation, and analysis of large volumes of data. You will collaborate with cross‑functional teams, including analysts, software engineers, and operations teams to ensure the availability, reliability, and integrity of data for various business needs. This role requires strong technical expertise in data engineering principles, database management, and programming skills.

We are looking for a candidate with good knowledge of Big Data technology and strong development experience with Databricks. You will help to migrate existing on‑prem applications to cloud (Azure preferred), create and maintain new data applications relying on experience and judgment to plan and accomplish goals, while working with other globally situated team members.

• Design, develop, and maintain data pipelines in Azure for ingesting, transforming, and loading data from various sources into centralized Azure data lakes and Databricks Delta Lake, ensuring data quality and integrity throughout the process.
• Implement efficient ELT/ETL processes to ensure data quality, consistency, and reliability, develop transformation processes to clean, aggregate, and enrich raw data, and integrate data from diverse sources to provide a unified view of information.
• Design and implement efficient data models and database schemas that support the storage and retrieval of structured and unstructured data, optimizing storage and access for performance and scalability.
• Leverage modern data pipeline tools to streamline data import/transformation processes, reduce human intervention during ETL, and ensure the efficiency and reliability of data ingestion and processing.
• Work closely with cross‑functional teams to understand data requirements and translate them into technical solutions.
• Monitor data pipelines, troubleshoot issues, and ensure data integrity and security.
• Implement data quality controls and validation processes to identify and rectify data anomalies, inconsistencies, and errors, and collaborate with stakeholders to define and enforce data governance standards and policies.
• Identify performance bottlenecks in data pipelines and database systems, optimize queries, data structures, and infrastructure configurations to improve overall system performance and scalability.
• Implement appropriate security measures to protect sensitive data, ensure compliance with data privacy regulations, and monitor and address data security vulnerabilities and risks.
• Collaborate with cross‑functional teams, including data scientists, analysts, and software engineers, to understand data requirements and deliver effective data solutions, documenting data engineering processes, data flows, and system configurations.
• Stay updated with the latest trends, tools, and technologies in the field of data engineering, proactively identify opportunities to improve data engineering practices, and contribute to the evolution of data infrastructure.
• Provide support to the development team with managing multiple instances of databases and servers, implementing complex queries with proper tuning, and guiding design decisions impacting data; manage data infrastructure in Azure (Data Lake, Data Warehouse and Synapse).

• Hands‑on experience with Azure services such as Azure Synapse Analytics, Azure Data Factory, Azure Databricks, Azure SQL Database, etc.
• Strong experience in data engineering, including data pipeline development, ETL/ELT processes, and data modeling.
• Strong proficiency in SQL and experience with programming languages such as Scala or Java.
• In‑depth knowledge of relational and non‑relational databases such as Snowflake, SQLServer, Oracle.
• Knowledge of data warehousing concepts, dimensional modeling, and best practices.
• Experience working in a multi‑developer environment, using version control (e.g., Git).
• Excellent…