Data Engineer

Working Hours : Full Time

Locations : Hyderabad

Experience : 4 –6 years

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About The Role

Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning & GenAI solutions—spanning predictive analytics, optimization, NLP, and enterprise-scale AI platforms—that help leading enterprises forecast, automate, and gain a competitive edge.

As a Data Engineer, you will build the foundation that powers these AI systems—scalable, secure, and high-performance data pipelines.

Job Overview

We seek a Data Engineer (Mid-level) with 4–6 years of hands-on experience in designing, building, and optimizing data pipelines. You will work closely with AI/ML teams to ensure data availability, quality, and performance for analytics and GenAI use cases.

Key Responsibilities

Data Pipeline Development:

• Build and maintain scalable ETL/ELT pipelines for structured and unstructured data
• Ingest data from diverse sources (APIs, streaming, batch systems).
  
  

Data Modeling & Warehousing

• Design efficient data models to support analytics and AI workloads.
• Develop and optimize data warehouses/lakes using Redshift, BigQuery, Snowflake, or Delta Lake.
  
  

Big Data & Streaming

• Work with distributed systems like Apache Spark, Kafka, or Flink for real-time/large-scale data processing.
• Manage feature stores for ML pipelines
  
  

Collaboration & Best Practices

• Work closely with Data Scientists and ML Engineers to ensure high-quality training data.
• Implement data quality checks, observability, and governance frameworks.
  
  

Required Skills & Qualifications

Education:Bachelor's/Master's in Computer Science, Data Engineering, or related field.

Experience: 4–6 years in data engineering with expertise in:

• Programming: Python/Scala/Java (Python preferred).
• Big Data & Processing: Apache Spark, Kafka, Hadoop.
• Databases: SQL/NoSQL (Postgres, MongoDB, Cassandra).
• Data Warehousing: Snowflake, Redshift, BigQuery, or similar.
• Orchestration: Airflow, Luigi, or similar.
• Cloud Platforms: AWS, Azure, or GCP (data services).
• Version Control & CI/CD: Git, Jenkins, GitHub Actions.
• MLOps/GenAI pipelines: (feature engineering, embeddings, vector DBs)
  
  

Skills Matrix

Candidates must submit a detailed resume and fill out the following matrix:

Skill

Details

Skills Last Used

Experience (months)

Self-Rating (0–10)

Python

SQL / NoSQL

Apache Spark

Kafka

Data Warehousing (Snowflake, Redshift, etc.)

Orchestration (Airflow, Luigi, etc.)

Cloud (AWS / Azure / GCP)

Data Quality / Governance Tools

MLOps / LLMOps

GenAI Integration

Instructions For Candidates

• Provide a detailed resume highlighting end-to-end data engineering projects.
• Fill out the above skills matrix with accurate dates, duration, and self-ratings.