Associate Team Lead - Data Engineer & Cloud | Devsinc

Devsinc; Lahore; On-site; PKR 1,800,000 - 3,200,000; Full time

Job summary

Devsinc is looking for an Associate Team Lead- Data Engineer to join our dynamic data team. You will design, build, and optimize data pipelines that transform raw data into insights, enabling data-driven decisions across the organization.

You will collaborate with data scientists, analysts, and stakeholders to define data requirements, ensure data quality, and implement robust data solutions using cloud services and data warehousing technologies. Strong leadership and communication are essential.

Qualifications

• Bachelor’s degree in CS, data engineering, or related field.
• 4+ years in data engineering building data pipelines.
• Strong SQL & relational DB experience.
• Proficient in Python or Scala for data processing.
• Experience with cloud data services and data warehousing.

Responsibilities

• Design, develop, and optimize scalable ETL pipelines.
• Collaborate with data scientists and analysts to define data requirements.
• Ensure data quality and implement validation/testing.
• Use cloud data services and warehousing for large data volumes.
• Monitor production pipelines and troubleshoot data issues.
• Contribute to data governance, security, and compliance.
• Stay updated with industry trends in data engineering.

Skills

SQL & relational databases

Python/Scala for data processing

Data modeling & ETL design

Problem solving & data quality

Communication with non-technical Stake

Education

Bachelor’s degree in CS/Data Eng

Tools

Apache Airflow

Talend

AWS Glue

Snowflake

Redshift

PostgreSQL

MySQL

AWS

Azure

GCP

Job description

Devsinc is on the lookout for a highly motivated Associate Team Lead- Data Engineer to join our dynamic data team. In this pivotal role, you will be responsible for building and optimizing data pipelines that transform raw data into valuable insights and facilitate data-driven decision-making. Your ability to work collaboratively with cross-functional teams and your expertise in data engineering will play a key role in shaping our data architecture and ensuring the integrity and accessibility of our data.

Key Responsibilities:

• Design, develop, and enhance scalable data pipelines and data models to streamline the extraction, transformation, and loading (ETL) processes.
• Collaborate with data scientists, analysts, and stakeholders to define data requirements and deliver robust data solutions that align with business goals.
• Ensure data quality, consistency, and accuracy by implementing validation and testing mechanisms.
• Utilize cloud services and data warehousing solutions to efficiently store and manage large volumes of data.
• Monitor and troubleshoot production data pipelines, providing timely resolutions to issues affecting data availability.
• Contribute to data governance initiatives by enforcing best practices related to data security, privacy, and compliance.
• Stay updated with industry trends and emerging technologies in the data engineering space.

Requirements

• Bachelor’s degree in Computer Science, Data Engineering, or a related field.
• 4+ years of experience in data engineering or a related role, with a proven track record in building data pipelines.
• Strong knowledge and experience in SQL and relational databases (e.g., PostgreSQL, MySQL).
• Familiarity with ETL tools and technologies (e.g., Apache Airflow, Talend, AWS Glue).
• Proficiency in programming languages such as Python or Scala for data processing.
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and their data services.
• Understanding of data warehousing concepts and experience with data storage technologies (e.g., Snowflake, Redshift).
• Excellent problem-solving skills with a focus on data integrity and performance optimization.
• Strong communication skills, with the ability to convey technical concepts to non-technical stakeholders.
• A collaborative spirit, with the ability to work closely with team members across various functions.
• A proactive approach to learning new tools and technologies in the data ecosystem.