principal specialist, data science

Randstad Staffing; Hyderabad; On-site; INR 1,200,000 - 2,400,000; Full time

Job summary

Randstad Staffing India Hyderabad seeks an experienced Data Engineer to design and build data pipelines and data storage solutions, collaborating with data scientists to deploy ML pipelines.

You will ensure data governance and privacy, contribute to the product roadmap, and mentor junior engineers while applying data engineering best practices.

Qualifications

• Masters degree in Computer Science or related field.
• Minimum 5 years of experience as a data engineer.
• Proficient in Python, SQL, and Java.
• Experience with GCP and/or AWS.
• Strong knowledge of Apache Beam and Spark.
• Familiar with dbt, Airflow, and CI/CD pipelines.
• Excellent English communication skills.

Responsibilities

• Designing and building data pipelines and data storage solutions across cloud platforms.
• Collaborating with data scientists to design and build ML pipelines.
• Ensuring compliance with data management principles, focusing on security and privacy.
• Defining and prioritizing work for the product roadmap and backlogs.
• Training and coaching junior data engineers.
• Collaborating within Randstad's data engineering community to share best practices.

Skills

Python

SQL

Java

Analytical thinking

English language

Education

Masters degree in Computer Science or related field

Tools

GCP

AWS

Apache Beam

Apache Spark

dbt

Airflow

GitLab

CI/CD

Job description

Every year, we help hundreds of thousands of people find rewarding jobs in the ever-changing world of work.

We understand the importance of a job in peoples lifes and we want to help them find work that feels good. And we’ll help them continue to grow as their needs and ambitions change.

At Randstad, our value comes from our people and that is why we put them first. We are proud of our learning culture and career architecture framework that encourages ours team to develop both personally and professionally.

We believe that talent grows when presented with opportunity and this is why we encourage our people to think beyond their role. We have created a culture that enables talent to flourish, encouraging entrepreneurship, fostering team spirit, and continually building mutual trust.

designing and building data pipelines and data storage solutions, using multiple database technologies across our GCP and AWS platforms (AWS/OpenSearch, BigQuery, Datastore, GCS)

collaborating with our data scientists to design and build ML pipelines, that bring our models alive

ensuring the full compliance of solutions being implemented with corporate data management principles, particularly pertaining to security and privacy

defining and appropriating work to the product roadmap and backlogs

training and coaching junior data engineers

collaborating within the data engineering community within the Randstad ecosystem, on sharing best practices, setting and deploying standards, and helping grow our collective data engineering skills

who will you work with

You will be a part of a highly engaged, international and agile team of highly skilled specialists, including data scientists, data engineers, machine learning engineers, and data analysts.

You will work closely with other data engineers within the Randstad ecosystem, both within projects as in the data engineering community.

what you will bring

Masters degree in Computer science or related technical studies (M.Sc. or equivalent experience)

min. 5 years work experience as a data engineer.

algorithmic thinking, analytical capabilities.

cloud platforms: comprehensive understanding of cloud computing (min. 3 years experience with GCP, AWS as a plus), solid experience in distributed computing.

coding: high proficiency in Python, SQL and Java; Scala is a plus

data processing: good knowledge on Apache Beam; Apache Spark as a plus.

data management: good grasp of data governance strategies, ETL tooling like dbt, orchestration (Airflow).

source control: experience working with CI/CD pipelines, gitlab, agile methodology.

ML engineering: experience with implementing MLOps practices and ML models deployments into production.

strong knowledge of data structures, data platforms and deployment at scale.

hands on experience on designing and building data pipelines, including transformation, processing and quality control.

broad knowledge on applying the latest DataOps concepts, implementation toolings and data management frameworks.

language skills: English (verbal and written)

skills

• Unsupervised Learning
• Supervised Learning
• SQL Databases
• Scrum (Software Development)
• Communication
• Artificial Intelligence Markup Language (AIML)