AWS Data Engineer

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.

Design, develop, and maintain ETL pipelines involving large-scale data.

Develop data processing and analytics applications primarily using PySpark and Python.

Build scalable and distributed data processing solutions using Apache Spark.

Develop and deploy data applications on AWS cloud.

Work with AWS services related to storage, compute, ETL, data warehousing, analytics, and streaming.

Implement distributed storage and processing solutions capable of handling high-volume datasets.

Design data processing applications with a focus on performance, scalability, reliability, and optimization.

Work with both SQL and NoSQL databases for data storage, processing, and analytics.

Write, optimize, and analyze SQL, HQL, and NoSQL queries.

Troubleshoot data pipeline and processing issues and ensure data quality and reliability.

Collaborate with data engineers, analysts, architects, and other technical teams to deliver data-driven solutions.

Data Engineer

Location: Bengaluru, India (Hybrid)

Employment Type: Full-time

Experience: 3-5 years

Role Overview  

What We’re Looking For:

• Bachelor’s degree in Computer Science/Engineering or equivalent experience required.
• Experience designing and shipping cloud services products.
• Experience driving and managing technical and architectural dependencies on AWS Cloud.
• A firm understanding of system architecture, cloud computing, PaaS/SaaS design principles, S3, DynamoDB, RDS mandatory.
• Experience in building or maintaining ETL processes and tools, i.e., AWS Glue or any open-source tool.
• Proven system-level design contribution to a current “Live” (in production / under daily high load) multi-region SaaS or PaaS offering.
• Proven experience with S3, DynamoDB, SQL, and AWS RDS services.
• Proficiency in programming languages such as Python.
• Strong analytical and problem-solving skills.
• 
  

Required Skills & Experience

• Experience with Python, SQL, and data visualization/exploration tools.
• Familiarity with the AWS ecosystem, specifically S3, DynamoDB, and RDS.
• Communication skills, especially for explaining technical concepts to nontechnical business leaders.
• Ability to work on a dynamic, research-oriented team that has concurrent projects.
• Experience in AWS cost optimization (Savings Plans, Reserved Instances, Spot Instances) and governance frameworks.
• Experience developing solutions using infrastructure orchestration tools (SSM, automation account, Ansible, etc.).
• Excellent leadership, stakeholder management, and communication skills.

What We Offer

• Work with some of the brightest minds in the emerging EV industry.
• Make a tangible impact in reducing carbon emissions and enabling sustainable energy.
• Freedom to suggest, implement, and innovate on systems, processes, and technologies.
• Daily ownership in a high-growth, challenging environment.
• Flexible work environment with hybrid schedules and virtualization options.
• Competitive pay and benefits including health coverage, innovative PTO program, and performance bonuses.

Job Title : Senior Data Engineer – Databricks

Experience : 14 to 20 Years

Location : HSR Layout, Bangalore

Work Mode : Hybrid – 3 Days WFO

Shift : 11:30 AM – 07:30 PM IST

Positions : 2

Notice Period : Immediate Joiners Only

Interview : 1 Technical Round + 2 Client Rounds

Role Overview :

We are looking for a Senior Data Engineer to build and lead enterprise-scale data platforms for a Switzerland-based commodity client.

The role requires a strong hands-on Data Engineering professional with expertise in Databricks, PySpark, Python, SQL, and AWS, along with technical leadership and stakeholder management experience.

Must-Have Skills :

• 14 to 20 years of Data Engineering experience
• Databricks & Apache Spark / PySpark
• Python & SQL
• AWS Cloud
• Lakehouse Architecture
• ETL / ELT & Distributed Data Processing
• Batch & Streaming Pipelines
• Data Pipeline Optimization & Data Modeling
• CDC & Incremental Processing
• Git, CI/CD & Testing
• Data Quality, Monitoring & Observability
• Technical Leadership & Stakeholder Management

Key Responsibilities :

• Design and build scalable data pipelines using Databricks, PySpark, Python, SQL, and AWS.
• Own data products from design through production.
• Develop batch / streaming pipelines and reusable ETL / ELT frameworks.
• Optimize pipelines for performance, scalability, reliability, and cost.
• Design scalable data architectures and data models.
• Implement data quality, monitoring, lineage, and CI/CD practices.
• Lead technical discussions and mentor engineering teams.
• Collaborate with business stakeholders, architects, product owners, and engineering teams.
• Remain hands-on while providing technical leadership.

Ideal Candidate :

A 14 to 20 years experienced, hands-on Data Engineering leader with strong Databricks + PySpark + AWS expertise, excellent communication, stakeholder management, and experience delivering enterprise-scale data platforms.

🔴 Super Urgent : Only Bangalore-based immediate joiners.

Job Summary

We are looking for a skilled and experienced Data Engineer to join our growing data team. The ideal candidate will have strong expertise in Python, PySpark, Data Modeling, and Power BI, with hands-on experience in designing, developing, and optimizing scalable data solutions. The role requires working closely with business stakeholders, data architects, and analytics teams to build robust data pipelines and semantic models that enable data-driven decision-making.

Technical Skills

• Strong hands-on experience in Python and PySpark development.
• Expertise in building and optimizing Data Engineering solutions and ETL pipelines.
• Strong understanding of Data Modeling concepts (Star Schema, Snowflake Schema, Dimensional Modeling).
• Experience with Power BI Data Modeling and Semantic Layer development.
• Proficiency in DAX (Data Analysis Expressions).
• Experience designing and managing Semantic Models in Power BI.
• Strong SQL skills and experience working with large datasets.
• Knowledge of data warehousing concepts and best practices.

Preferred Skills

• Experience with cloud platforms such as Azure, AWS, or GCP.
• Exposure to modern data platforms like Databricks.
• Understanding of data governance and data quality frameworks.

Job Title: Data Engineer – PySpark | Oracle | GCP

Experience: 5–7 Years

Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Job Summary

We are seeking an experienced Data Engineer with strong expertise in PySpark, Oracle, and Google Cloud Platform (GCP) to design, develop, and optimize scalable data pipelines. The ideal candidate should have hands-on experience in ETL development, data integration, and cloud-based data engineering solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL/data pipelines using PySpark.
• Extract, transform, and load data from Oracle databases into GCP environments.
• Build and optimize batch data processing workflows for high performance and reliability.
• Develop data engineering solutions using GCP services.
• Ensure data quality through validation, monitoring, and troubleshooting.
• Optimize SQL queries and ETL jobs for performance and scalability.

Required Skills

• 5–7 years of experience as a Data Engineer.
• Strong hands-on experience with PySpark.
• Solid experience with Oracle Database and advanced SQL.
• Hands-on experience with Google Cloud Platform (GCP).
• Strong understanding of ETL processes and data warehousing concepts.

Work Location: Hyderabad

Notice Period: Immediate Joiners Preferred

Job Description – Lead Data Engineer (AWS + Big Data)

Location: Pune - Hybrid

Experience: 8+ Years

Role Overview

We are looking for a Lead Data Engineer with strong hands-on expertise in AWS-based Big Data platforms. The ideal candidate should have extensive implementation experience in designing and building scalable data pipelines, mentoring engineering teams, and driving technical delivery. Databricks exposure is mandatory, while deep implementation experience in Databricks is not essential. Candidates with HBase experience will be preferred.

Key Responsibilities

• Design, develop, and maintain scalable data pipelines and ETL/ELT solutions on AWS.
• Build and optimize Big Data applications using Spark/PySpark, HBase, Hive, Kafka, and related technologies.
• Work with AWS services such as S3, Glue, Lambda, IAM, and CloudWatch for cloud-native data engineering.
• Lead technical implementation, mentor engineers, conduct code reviews, and drive engineering best practices.
• Ensure data quality, performance optimization, CI/CD adoption, and production support.

Required Skills

• Strong hands-on experience with AWS (S3, Glue, Lambda, IAM, CloudWatch)
• Apache Spark (PySpark/Scala) and Big Data ecosystem
• Databricks exposure (mandatory)
• HBase (strongly preferred)
• Python or Java, Advanced SQL
• ETL/ELT development and Data Warehousing concepts
• Apache Airflow or similar orchestration tools
• Git, CI/CD, Performance Tuning, and Data Quality

Good to Have

• Kafka / Spark Structured Streaming
• Hive, Impala, Hadoop ecosystem
• Delta Lake / Lakehouse concepts
• Snowflake or other modern cloud data platforms

Experience Required

• 8–14 years of Data Engineering experience with strong AWS implementation expertise.
• Proven experience leading technical delivery and mentoring engineering teams.
• Strong understanding of enterprise-scale data platforms and Big Data architectures.
• Ability to collaborate with architects, stakeholders, and cross-functional teams to deliver scalable solutions.

NOTE: Final Technical round is mandatory to be taken F2F from Pune, office.

Description

We are looking for Senior Data Engineers to join our Data Platform team and build scalable, high-performance data platforms that power data processing, analytics, and downstream applications.

The ideal candidate will have strong experience in distributed data processing, ETL pipelines, and Big Data technologies, with hands-on expertise in Apache Spark and Python Scala.

You will be responsible for designing, developing, and optimizing large-scale data pipelines while collaborating closely with cross-functional engineering teams to build reliable, production-grade data solutions.

Key Responsibilities

• Design, develop, and maintain scalable ETL and data processing pipelines for large-scale datasets.
• Build and optimize distributed data applications using Apache Spark and Python Scala.
• Develop reliable, high-performance data pipelines for batch and streaming workloads.
• Design and manage data workflows using Apache Airflow.
• Build and operate data workloads on AWS, with strong usage of Amazon S3 for large-scale data storage.
• Work with large datasets to ensure data quality, consistency, reliability, and performance.
• Collaborate with engineering, product, analytics, and other platform teams to deliver robust data solutions.
• Optimize data workflows for scalability, reliability, performance, and cost efficiency.
• Troubleshoot production issues, identify bottlenecks, and continuously improve platform performance.

Requirements

Candidates who demonstrate:

• 5+ years of experience in Data Engineering, Big Data Engineering, or a similar role.
• Strong hands-on experience with Apache Spark and Scala.
• Experience designing, building, and maintaining large-scale ETL pipelines.
• Strong hands-on experience with AWS, particularly Amazon S3.
• Hands-on experience with Apache Airflow for workflow orchestration and scheduling.
• Strong SQL skills and a solid understanding of distributed data processing concepts.
• Experience working with batch and/or streaming data pipelines.
• Excellent debugging, problem-solving, and performance optimization skills.
• Strong communication and collaboration skills.

Good to Have

• Experience with Databricks and the broader Databricks data platform.
• Familiarity with streaming technologies such as Apache Kafka.
• Experience working on large-scale data platforms handling high-volume data workloads.
• Exposure to additional AWS data services and cloud-native data architectures.

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.

Skills Referential (Required knowledge, skills and abilities)

Technical Skills:

Python

Pyspark

SQL

ETL Aws, Azure, gcp