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Data Engineers lead is responsible for leading a team in offshore (Viet Nam, India) to developing and
operationalizing data pipelines to make data available for consumption (reports and advanced analytics).
This includes data ingestion, data transformation, data validation/quality, data pipeline optimization,
orchestration; and engaging with DevOps Engineer during CI/CD.
The role requires leading skills and grounding in programming and SQL, followed by expertise in data
storage, modeling, cloud, data warehousing, and data lakes, ETL tools. The Data Engineer Lead will work
closely with customer Data Architects, Data Scientists and BI Engineers to design and maintain scalable
data models and pipelines.
Data engineer lead will transfer requirements to the offshore team, lead the team to ensure the project
delivers high quality products to customers.
The Data Engineer Lead also spends time developing data pipelines and/or data integrations stack.
Job Duties/Essential Functions
● Leads DE team with one offshore member but can ramp-up sooner. This includes providing
guidelines for team members, reviewing output, and the quality of team members.
● Work as bridge between customer and offshore team. Present solution to customer, get feedback
work with team to update accordingly, as well as transfer requirement, feedback from customer in a
clear manner.
● Lead technical design activity from project team, ensure the output, quality meet, exceed customer’s
expectation.
● Build data models and develop data pipelines to store data in defined data models and structures.
● Identifies ways to improve data reliability, efficiency and quality of data management.
● Assesses the integrity of data from multiple sources.
● Works in tandem with Data Architects, Data Stewards and Data Quality Engineers to design data
pipelines and recommends ongoing optimization of data storage, data ingestion, data quality and
orchestration.
● Azure experience is a plus and Experience with Dataflow, Apache Beam, and Dataproc
● Designs, develops, & implements ETL/ELT processes using Dbt (Data build tool), Informatica
Intelligent Cloud Services (IICS).
● Uses Google Cloud Google cloud services such as Google Cloud Storage, Cloud Pub/Sub, Cloud
Data Fusion, Dataflow, Dataproc… to improve and speed up delivery of data products and services.
Experience, Skills, Education & Licenses/Certifications
Required:
● 8+ years’ experience engineering and operationalizing data pipelines with large and complex
datasets.
● 8+ years’ hands-on experience with DBT and IICS.
● 7+ years’ experience working with Cloud technologies such as Data flow, Data Fusion, Pub/Sub,
Dataform, dbt, Bigquery, Cloud SQL
● Advanced SQL skills required. Solid understanding of relational databases and business data; ability
to write complex SQL queries against a variety of data sources.
● 7+ years’ experience with Data Modeling, ETL, and Data Warehousing.
● Strong understanding of database storage concepts (data lake, relational databases, NoSQL, Graph,
data warehousing).
Most IT Jobs posts in Hyderabad ask for 3–6 years of experience (44% of 1009 that say).
From live job posts on CoreHRx, updated 4 Nov 2026. Skill figures count posts that list their skills; salary figures only posts that show a salary.
The post does not state the pay. As an estimate, comparable posts on CoreHRx advertise ₹9L – ₹21L a year (typical for IT Jobs in Hyderabad, 76 posts).
The post asks for 8 Yrs experience.
In Hyderabad.
It is a full time job.
You apply on TechVedika's website. CoreHRx takes you there after you sign in.
Interview preparation · 60 questions
This guide is for Indian job seekers preparing for SQL rounds in data analyst, data engineer, backend developer and database roles, from freshers to experienced professionals. It has 60 questions in three levels: easy covers joins, grouping and NULLs; medium covers window functions, CTEs, indexes and isolation levels; hard covers locking, MVCC, query tuning and classic puzzles such as login streaks and medians. Answers use ANSI SQL, with MySQL 8 and PostgreSQL notes where the two differ. Write each query against your own sample tables first, then compare your result with the sample answer.
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