GCP Data Engineer - Chennai - GOM at Getinz Techno Services

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

26 May, 26

Salary

0.0

Posted On

25 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

GCP, BigQuery, Terraform, Python, SQL, Data Pipeline Development, Cloud Storage, Dataproc, Data Flow, Cloud Composer, Cloud Sql, Pub Sub, Hadoop Ecosystem, Kafka, PCF Cloud Services, Agile

Industry

Staffing and Recruiting

Description

Job Title: GCP Data Engineer Experience: 3-5 Years Location: Chennai – Hybrid (4 Days/Week) About the Company: Our client is a global digital transformation and marketing technology services firm that helps enterprises deliver personalized, data-driven customer experiences. With strong capabilities across engineering, cloud, data, and experience design, the company partners with leading brands to build scalable, high-performance digital platforms. Their teams work on complex, high-traffic web applications using modern architectures and cloud-native technologies. Role Overview: This role is responsible for designing, building, and optimizing scalable data pipelines to support advanced analytics and business insights. The position works closely with data scientists, engineers, and business stakeholders to translate requirements into robust cloud-based data solutions. The ideal candidate has strong hands-on experience with GCP (especially BigQuery), Terraform, Python, and advanced SQL, along with expertise in data pipeline development within big data environments. The role also involves improving data quality, preventing recurring data issues, mentoring team members, and delivering reliable solutions in an agile, fast-paced setting. Key Responsibilities: Work with data scientists and software engineers to support data quality initiatives, data solution ideation, and implementation Work with technical and business leads to transfer global business requirements into sound solutions and implementation Share support responsibilities for implemented components Required Skills: 3+ years of demonstrated ability with Hive, Python, Spark/Scala, SQL, etc. Google Cloud Platform Experience, Big Query, Cloud Storage, Dataproc, Data Flow, Cloud Composer, Cloud SQL, Pub Sub, Terraform, etc. Experience with Hadoop Ecosystem, Kafka, PCF cloud services Familiar with big data and machine learning tools and platforms Experience with BI tools, such as Alteryx, Data Stage, QlikSense, etc. Design data pipelines and data robots, take a vision and bring it to life Master data engineer; mentors others; works closely with IT architects to set strategy and design projects Provide extensive technical, and strategic advice and guidance to key stakeholders around the data transformation efforts Redesign data flows to prevent recurring data issues Strong analytical and problem-solving skills Possess excellent oral and written communication skills, as well as facilitation and presentation skills, and engaging presentation style. Ability to work as a global team member, as well as independently, in a changing environment and prioritize. Ability to establish and maintain coordinated and effective working relationships with application implementation teams, IT project teams, business customers, and end users. Ability to deliver work within deadlines. Experience with agile/lean methodologies Experience working independently and with minimal supervision Experience with Test Driven Development and Software Craftsmanship Experience with GitHub, Accurev, or other version-control systems Experience with Putty Experience with Datastage Strong Communications skills Ability to illustrate and convey ideas and prototypes effectively with team and partners Presence demonstrating confidence, ability to learn quickly, influence, and shape ideas Eligibility / Qualifications: Education: Bachelor’s degree in IT, Computer Science, Engineering, or equivalent. Other Details: Notice Period: Immediate joiners Work Type: [Full-time]

Responsibilities

This role involves designing, building, and optimizing scalable data pipelines to support advanced analytics and business insights, working closely with data scientists, engineers, and business stakeholders. Key tasks include translating requirements into robust cloud-based data solutions and improving data quality to prevent recurring issues.