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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:

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.