Senior Data Engineer

Cargill

4.1

Bengaluru, India

4.1/5 in overall job satisfaction

4.5/5 in supportive management

71% say women are treated fairly and equally to men

71% would recommend this company to other women

79% say the CEO supports gender diversity

Ratings are based on anonymous reviews by Fairygodboss members.

Cargill partners with Milk Stork; a service that provides no-fuss, refrigerated, express shipping or easy toting of breast milk

All employees have access to Bright Horizons for child, pets, and eldercare resources. Including free Sittercity memberships & discounts

Our 401(k) offers immediate eligibility & matching contributions of 100% on the first 3% you contribute, then 50% on the next 2%

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n of data from various data sources, transform the data into information and move to data stores like data lake, data warehouse and others.

DATA SYSTEMS: Reviews existing data systems and architectures to identify areas for improvement and optimization.
STAKEHOLDER MANAGEMENT: Collaborates with multi-functional data and advanced analytic teams to gain requirements and ensure that data solutions meet the functional and non-functional needs of various partners.
DATA FRAMEWORKS: Builds complex prototypes to test new concepts and implements data engineering frameworks and architectures that improve data processing capabilities and support advanced analytics initiatives.
AUTOMATED DEPLOYMENT PIPELINES: Develops automated deployment pipelines improving efficiency of code deployments with fit for purpose governance.
DATA MODELING: Performs complex data modeling in accordance to the datastore technology to ensure sustainable performance and accessibility.

Qualifications

Bachelor's degree in Computer Science, Engineering, or a related technical field with minimum 8 years of work experience.
CLOUD ENVIRONMENTS: Experience developing data systems on major cloud platforms (AWS, GCP, Azure).
DATA ARCHITECTURE: Hands-on experience building modern data architectures, including data lakes, data lakehouses, and data hubs, along with related capabilities such as ingestion, governance, modeling, and observability.
DATA INGESTION: Demonstrated proficiency in data collection, ingestion tools (Kafka, AWS Glue), and storage formats (Iceberg, Parquet).
DATA STREAMING: Experience developing data pipelines with streaming architectures and tools (Kafka, Flink).
DATA MODELING: Expertise in data transformation and modeling using SQL-based frameworks and orchestration tools (dbt, AWS Glue, Airflow). Deep experience with modeling concepts like SCD and schema evolution.
DATA TRANSFORMATION: Strong background with using Spark for data transformation, including streaming, performance tuning, and debugging with Spark UI.
PROGRAMMING: Advanced programming skills in Python, Java, Scala, or similar languages. Expert-level proficiency in SQL for data manipulation and optimization.
DEVOPS: Demonstrated experience in DevOps practices, including code management, CI/CD, and deployment strategies.
DATA GOVERNANCE: Strong background in data governance principles, including data quality, privacy, and security considerations for data product development and consumption.

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