Data Engineer

Job Description

We are looking for an experienced Data Engineer with strong expertise in Python, ETL, SQL, CI/CD, and DevOps to design, develop, and maintain scalable data pipelines and data processing solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines.
• Develop data processing solutions using Python.
• Write complex and optimized SQL queries, stored procedures, and data transformations.
• Build and maintain data ingestion and integration workflows.
• Implement data quality, validation, monitoring, and error-handling processes.
• Develop and maintain CI/CD pipelines for data engineering applications.
• Work with DevOps tools and practices for automated build, deployment, and infrastructure management.
• Collaborate with data analysts, data scientists, software engineers, and business teams.
• Optimize data pipelines for performance, reliability, and scalability.
• Troubleshoot production data issues and ensure timely resolution.
• Follow best practices for version control, code quality, testing, and deployment.

Mandatory Skills

• Python
• ETL
• SQL
• CI/CD
• DevOps
• Git / Version Control
• Strong problem-solving and debugging skills

• Design, build, and maintain scalable ETL/ELT pipelines for batch and real-time data ingestion and transformation.
• Develop and optimize data lake and data warehouse architectures (e.g., Snowflake, BigQuery, Redshift).
• Work with cloud platforms GCP, Azure to manage data infrastructure.
• GCP as mandatory skills
• Collaborate with analytics and product teams to understand data needs and deliver solutions.
• Ensure data quality, reliability, security, and compliance across all data systems.
• Mentor junior data engineers and contribute to best practices and code reviews.
• Monitor and troubleshoot data pipeline performance and resolve data-related issues.
• Automate data validation, monitoring, and alerting processes.
• 8+ years of experience in data engineering or software engineering with a data focus.
• Proficient in SQL and at least one programming language (e.g., Python, Scala, Java).
• Experience with modern data warehousing tools (e.g., Snowflake, Redshift, BigQuery).
• Strong understanding of data modeling, data lakes, and ETL/ELT design.
• Hands-on experience with orchestration tools like Airflow, dbt, or similar.
• Solid experience with cloud data platforms (AWS/GCP/Azure).
• Familiarity with CI/CD pipelines, containerization (Docker/Kubernetes), and version control (Git).
• Experience working in a DevOps or DataOps environment.
• Knowledge of data governance, lineage, and cataloging tools (e.g., Collibra, Alation).
• Familiarity with streaming technologies (Kafka, Spark Streaming, Flink).
• Experience supporting machine learning workflows and data science initiatives.

Job Summary

We are seeking a highly skilled GCP Data Engineer with strong expertise in Google Cloud Platform (GCP), Python, ETL, and modern data engineering technologies. The ideal candidate should have hands-on experience designing and building scalable data pipelines using BigQuery, Dataflow, Pub/Sub, Airflow, and modern data lake technologies such as Apache Iceberg or Delta Lake.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines on Google Cloud Platform.
• Build and optimize data processing workflows using Python and Google Cloud Dataflow (Apache Beam).
• Develop and manage large-scale analytical data models in BigQuery.
• Implement event-driven data ingestion using Google Cloud Pub/Sub.
• Create, schedule, and monitor workflows using Apache Airflow and Autosys.
• Design and implement modern data lake architectures using Apache Iceberg or Delta Lake.
• Optimize query performance, storage, and compute costs in GCP.
• Ensure data quality, governance, security, and compliance across data platforms.
• Collaborate with Data Scientists, Analysts, and Application teams to deliver scalable data solutions.
• Troubleshoot production issues and continuously improve pipeline reliability and performance.

Mandatory Skills

• Strong hands-on experience with Google Cloud Platform (GCP).
• Proficiency in Python programming.
• Experience in designing and implementing ETL/ELT pipelines.
• Strong knowledge of BigQuery.
• Experience with Google Cloud Dataflow (Apache Beam).
• Experience with Google Cloud Pub/Sub.
• Hands-on experience with Apache Airflow.
• Experience in job scheduling using Autosys.
• Experience with modern table formats such as Apache Iceberg or Delta Lake.
• Strong SQL and data modeling skills.

Preferred Skills

• Experience with Cloud Storage, Dataproc, Cloud Composer, and Cloud Functions.
• Knowledge of CI/CD pipelines and DevOps practices.
• Experience with Docker and Kubernetes.
• Familiarity with Git and Agile/Scrum methodologies.
• Knowledge of data warehousing and dimensional modeling.
• Exposure to streaming and real-time data processing.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
• 4–8+ years of experience in Data Engineering with hands-on expertise in GCP technologies.

Required Experience

• Strong experience in developing enterprise-grade data pipelines using Python and GCP.
• Hands-on experience with BigQuery, Dataflow, Pub/Sub, and Airflow.
• Experience scheduling and monitoring batch workflows using Autosys.
• Experience implementing modern data lake architectures using Apache Iceberg or Delta Lake.
• Strong understanding of ETL best practices, performance tuning, and data optimization.
• Excellent analytical, troubleshooting, and problem-solving skills.

Mandatory Skills

• Google Cloud Platform (GCP)
• Python
• ETL
• BigQuery
• Autosys
• Apache Airflow
• Google Cloud Pub/Sub
• Google Cloud Dataflow (Apache Beam)
• Apache Iceberg / Delta Lake
• SQL & Data Modeling

Data Engineer Short Hiring Post

🚨 Hiring: Data Engineer

🔹 Experience: 5–9 Years

🔹 Location: Bangalore / Hyderabad

🔹 Skills: PySpark, Python, SQL, ETL, CI/CD, Data Modeling

🔹 Process: L1 Virtual → L2 F2F Karat Test

🔹 F2F: Bangalore / Hyderabad Location

🔹 Positions: Immediate requirement

⚠️ Note: Candidates must be available for F2F Karat immediately after L1.

#Hiring #DataEngineer #PySpark #Python #SQL #BangaloreJobs #HyderabadJobs #Mphasis #ImmediateJoiners

Job Summary

We are seeking a skilled Data Engineer to design, build, and maintain scalable data pipelines and infrastructure. The ideal candidate should have strong expertise in SQL, Python, Linux, and modern data engineering practices to support data integration, transformation, and analytics.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Write efficient and optimized SQL queries for data extraction, transformation, and reporting.
• Develop automation scripts using Python for data processing and workflow optimization.
• Work with Linux environments for deployment, monitoring, and troubleshooting.
• Ensure data quality, integrity, and reliability across data platforms.
• Collaborate with data analysts, software engineers, and business stakeholders to deliver data solutions.
• Monitor, troubleshoot, and optimize data pipelines for performance and scalability.
• Implement best practices for data security, governance, and documentation.

Required Skills

• Strong experience in Data Engineering concepts and ETL/ELT processes.
• Proficiency in SQL, including query optimization and database design.
• Strong programming skills in Python.
• Hands-on experience with Linux commands, shell scripting, and system administration basics.
• Experience with relational databases such as PostgreSQL, MySQL, SQL Server, or Oracle.
• Familiarity with Git/version control.
• Strong analytical and problem-solving skills.

Preferred Skills

• Experience with cloud platforms (AWS, Azure, or GCP).
• Knowledge of Apache Spark, Airflow, Kafka, or similar data engineering tools.
• Experience with data warehousing solutions and big data technologies.
• Understanding of CI/CD pipelines and containerization (Docker/Kubernetes).

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• Relevant certifications in cloud or data engineering are an added advantage.

Role Overview

We are looking for a GCP Data Engineer with 10+ years of experience to design, develop, and optimize scalable cloud-based data solutions. The ideal candidate will have strong hands-on expertise in GCP, BigQuery, and advanced SQL, with experience building data pipelines and working with large-scale datasets.

Key Responsibilities

• Design and develop scalable data pipelines and ETL/ELT processes on GCP.
• Build, optimize, and maintain data solutions using Google BigQuery.
• Develop complex SQL queries for data transformation, aggregation, and analysis.
• Design efficient data models and optimize pipelines for performance, scalability, and cost.
• Integrate data from multiple sources and ensure data quality, reliability, and availability.
• Troubleshoot pipeline and data issues and drive continuous improvement.
• Collaborate with data architects, analysts, application teams, and business stakeholders.
• Follow best practices for cloud security, data governance, testing, and documentation.

Required Skills

• 8+ years of Data Engineering experience
• Strong hands-on experience with GCP, Django, and MongoDB
• Extensive experience with BigQuery
• Advanced SQL skills
• Strong understanding of ETL/ELT and data pipeline development
• Data modeling and data warehousing experience
• Experience handling large-scale datasets and performance optimization
• Strong problem-solving and communication skills

Good to Have

• GCP services such as Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, or Cloud Functions
• Python or other data engineering languages
• Experience with data governance and security
• Agile development experience

Experience: 6+ years overall Data Engineering experience.

Must-have — candidates should have hands-on experience in ALL of these:

1. GCP (Google Cloud Platform) – strong hands-on experience
2. Python – data engineering/ETL development
3. SQL – advanced SQL, query optimization, data transformation
4. BigQuery – strong hands-on experience with development, optimization and data warehousing
5. Data Engineering / ETL – building and maintaining data pipelines
6. GCP data services – preferably Cloud Storage, Dataflow, Pub/Sub, Composer/Airflow, etc.
7. Data warehousing / dimensional modeling

Job Summary

We are seeking a motivated Data Engineer with strong skills in SQL, Python, and Linux to design, build, and maintain scalable data pipelines and support data-driven decision-making. The ideal candidate should have experience working with large datasets, ETL processes, and relational databases while ensuring data quality and performance.

Key Responsibilities

• Design, develop, and maintain ETL/ELT data pipelines.
• Write optimized SQL queries, stored procedures, and database objects.
• Develop Python scripts for data extraction, transformation, and automation.
• Work in Linux environments to manage scripts, cron jobs, and system processes.
• Monitor and troubleshoot data pipeline failures.
• Ensure data integrity, consistency, and quality across systems.
• Collaborate with data analysts, software engineers, and business stakeholders.
• Optimize database performance and query execution.
• Participate in code reviews and follow best engineering practices.

Required Skills

• Strong proficiency in SQL (joins, subqueries, window functions, CTEs, indexing, query optimization).
• Good programming experience in Python.
• Hands-on experience with Linux commands and shell scripting.
• Understanding of ETL/ELT concepts and data warehousing.
• Knowledge of relational databases such as PostgreSQL, MySQL, Oracle, or SQL Server.
• Familiarity with Git for version control.
• Strong problem-solving and analytical skills.

Role Summary

We are hiring a Data Engineer / ML Data Pipeline Engineer to build and operate the data backbone of the Enterprise AI platform:

What You'll Own

• Ingestion & ETL/ELT pipelines for heterogeneous project folders (PDF drawings, SVG files, IFC models, BBS.json bar-bending-schedule data, Excel exports, and AI agent output JSON).
• AWS-based data architecture: S3 raw/staging/curated/outputs structuring, partitioning, versioning, and lifecycle management; querying via Athena/Glue and warehousing via Redshift or Snowflake as needed.
• Data validation frameworks: GUID cross-referencing between SVG and BBS data, schema enforcement, duplicate/orphan detection, reference integrity checks, and structured validation reporting.
• Agent run logging & observability: designing the database schema and pipelines that track every AI agent run (inputs, outputs, status, errors, cost, retries, reviewer feedback).
• AI Factory monitoring dashboards: operational dashboards (failure rates, retries, latency, data quality) and business dashboards (throughput, cost per run, rework rate) for Power BI/QuickSight or equivalent.
• ML data pipeline support: dataset preparation, labeling/annotation workflows, human-in-the-loop review tooling, and dataset versioning for models that classify or QC drawing issues.
• APIs: designing and building FastAPI/Flask endpoints to trigger validation runs and expose agent processing status to internal tools.
• Data quality & testing discipline: idempotent pipelines, quarantine/reject handling, regression and reconciliation testing, and root-cause debugging when pipelines or query performance degrade in production.

Key Skills — Non-Negotiable (Must-Have, Strong Level)

• Python — production-grade scripting: file/folder handling, JSON/schema processing, clean error handling, not just notebook-level scripting.
• SQL — strong hands-on ability, including GROUP BY/HAVING for duplicate detection, window functions, and daily aggregate/rate calculations (e.g., success-rate queries).
• AWS S3 data handling — practical experience structuring buckets for raw/staging/curated data, versioning, and avoiding overwrite issues at scale.
• Data validation — demonstrable experience building validation logic (set comparisons, duplicate/missing detection, structured pass/fail reporting), not just "I write assertions."
• ETL/ELT pipeline design — end-to-end ownership of at least one pipeline: source → transform → storage → validation → monitoring → business outcome, with clear articulation of what they personally built.
• Query/warehouse engine judgment — working knowledge of when to use Athena vs. Redshift vs. Snowflake (or equivalent), partitioning, clustering, sort/distribution keys, and storage format trade-offs (Parquet vs. JSON vs. CSV).

Key Skills — Good to Have

• Dashboarding — Power BI / QuickSight (or equivalent) fact/dimension table design, KPI cards, drill-downs; medium-to-strong level is a plus but trainable.
• FastAPI / Flask — building real endpoints with request/response schemas and basic error handling; especially valuable for validation-trigger and agent-status APIs.
• ML data pipeline experience — dataset labeling, annotation platform design, train/test/validation splitting, dataset versioning; strong on the pipeline/data side rather than model training itself.
• Human-in-the-loop / review tooling — experience building or contributing to browser-based labeling/review platforms (session persistence, label schema, export formats).
• Large-scale metadata querying — experience making file discovery fast across large volumes (1,000+ projects, thousands of files each) via metadata index tables, event-based ingestion, or catalog tools like AWS Glue.

About AuxoAI:

AuxoAI is a global platform-based services firm. We help companies—turn their strategies into practical digital and AI solutions. By understanding how our clients make decisions, we use digital and Artificial Intelligence (AI) technologies to drive growth, enhance their operations, improve customer experiences, and provide clear, actionable insights from their data. What We Do We work across various industries such as healthcare, high-tech, consumer packaged goods (CPG), finance etc., and in sales, marketing, and customer support functions.

We help our clients with accelerating their digital and AI journeys through:

• AI Application Development

• Data, Digital and Cloud acceleration using AI

• AI Native Product Engineering

We are seeking a skilled and experienced Data Engineer to join our dynamic team. The ideal candidate will have 6+ years of prior experience in data engineering, with a strong background in AWS (Amazon Web Services) technologies. This role offers an exciting opportunity to work on diverse projects, collaborating with cross-functional teams to design, build, and optimize data pipelines and infrastructure.

Responsibilities:

* Design, develop, and maintain scalable data pipelines and ETL processes leveraging AWS services such as S3, Glue, EMR, Lambda, and Redshift.

* Collaborate with data scientists and analysts to understand data requirements and implement solutions that support analytics and machine learning initiatives.

* Optimize data storage and retrieval mechanisms to ensure performance, reliability, and cost-effectiveness.

* Implement data governance and security best practices to ensure compliance and data integrity.

* Troubleshoot and debug data pipeline issues, providing timely resolution and proactive monitoring.

* Stay abreast of emerging technologies and industry trends, recommending innovative solutions to enhance data engineering capabilities.

Requirements :

* Bachelor's or Master's degree in Computer Science, Engineering, or a related field.

* 6+ years of prior experience in data engineering, with a focus on designing and building data pipelines.

* Proficiency in AWS services, particularly S3, Glue, EMR, Lambda, and Redshift.

* Strong programming skills in languages such as Python, Java, or Scala.

* Experience with SQL and NoSQL databases, data warehousing concepts, and big data technologies.

* Familiarity with containerization technologies (e.g., Docker, Kubernetes) and orchestration tools (e.g., Apache Airflow) is a plus.