Lead Data Engineer

The ideal candidate will be responsible for designing and developing reliable, high-performance data pipelines, building ETL/ELT workflows, and implementing scalable data architectures and models.

The role involves working with SQL, Python, Apache Spark, data warehouses, data lakes, and cloud platforms such as AWS, Azure, or GCP, while optimizing data processing, storage, and integration across multiple systems.

Key Responsibilities

Lead the design, development, and delivery of scalable data engineering solutions and enterprise data platforms.

Design and optimize ETL/ELT pipelines for ingesting, transforming, and processing large volumes of data.

Work with different data integration, processing, storage, and cloud technologies based on project requirements.

Provide technical leadership, conduct code/design reviews, and mentor data engineering team members.

Collaborate with clients, stakeholders, architects, and cross-functional teams to understand requirements and deliver effective data solutions.

Drive data quality, performance, reliability, scalability, and monitoring across data pipelines.

Own technical delivery, manage dependencies and risks, and ensure projects are delivered within defined timelines.

Contribute to architecture decisions, best practices, documentation, and continuous improvement.

Required Skills & Qualifications

7+ years of hands-on experience in Data Engineering, with experience in a lead/technical leadership capacity.

Strong fundamentals in SQL, data modelling, ETL/ELT, data pipelines, and data warehousing.

Experience with any major cloud platform such as AWS, Azure, or GCP.

Experience with any enterprise data integration/ETL technology such as Informatica, Azure Data Factory, AWS Glue, SSIS, Talend, or similar.

Experience with one or more data processing/platform technologies such as Databricks, Snowflake, Spark, Synapse, Redshift, BigQuery, or equivalent.

Strong understanding of data architecture, scalability, performance optimization, and data quality.

Excellent problem-solving, communication, stakeholder management, and leadership skills.

Ability to understand business requirements and translate them into robust technical data solutions.

Nice to Have

Experience in Pharmaceutical, Healthcare, or Life Sciences domains.

Experience working with large-scale enterprise data platforms.

Exposure to modern data architecture, cloud migration, data lakes/lakehouses, or real-time data processing.