Data Engineer- Integration at SteelEye · Bengaluru (Bangalore) · 4 - 8 years · ₹20L - ₹30L / yr · Raised funding · Posted 14 Jul 2022
PythonAbout us
SteelEye is the only regulatory compliance technology and data analytics firm that offers transaction reporting, record keeping, trade reconstruction, best execution and data insight in one comprehensive solution. The firm’s scalable secure data storage platform offers encryption at rest and in flight and best-in-class analytics to help financial firms meet regulatory obligations and gain competitive advantage.
The company has a highly experienced management team and a strong board, who have decades of technology and management experience and worked in senior positions at many leading international financial businesses. We are a young company that shares a commitment to learning, being smart, working hard and being honest in all we do and striving to do that better each day. We value all our colleagues equally and everyone should feel able to speak up, propose an idea, point out a mistake and feel safe, happy and be themselves at work.
Being part of a start-up can be equally exciting as it is challenging. You will be part of the SteelEye team not just because of your talent but also because of your entrepreneurial flare which we thrive on at SteelEye. This means we want you to be curious, contribute, ask questions and share ideas. We encourage you to get involved in helping shape our business. What you'll do
What you will do?
- Deliver plugins for our python based ETL pipelines.
- Deliver python services for provisioning and managing cloud infrastructure.
- Design, Develop, Unit Test, and Support code in production.
- Deal with challenges associated with large volumes of data.
- Manage expectations with internal stakeholders and context switch between multiple deliverables as priorities change.
- Thrive in an environment that uses AWS and Elasticsearch extensively.
- Keep abreast of technology and contribute to the evolution of the product.
- Champion best practices and provide mentorship.
What we're looking for
- Python 3.
- Python libraries used for data (such as pandas, numpy).
- AWS.
- Elasticsearch.
- Performance tuning.
- Object Oriented Design and Modelling.
- Delivering complex software, ideally in a FinTech setting.
- CI/CD tools.
- Knowledge of design patterns.
- Sharp analytical and problem-solving skills.
- Strong sense of ownership.
- Demonstrable desire to learn and grow.
- Excellent written and oral communication skills.
- Mature collaboration and mentoring abilities.
What will you get?
- This is an individual contributor role. So, if you are someone who loves to code and solve complex problems and build amazing products and not worry about anything else, this is the role for you.
- You will have the chance to learn from the best in the business who have worked across the world and are technology geeks.
- Company that always appreciates ownership and initiative. If you are someone who is full of ideas, this role is for you.
Shubham Vishwakarma
Full Stack Developer - Averlon
About SteelEye
About
Our clients can aggregate, search, surveillance and report on trade, communications and market data. SteelEye also enables customers to gain powerful insights from their data, helping them to trade with greater efficiency and profitability. The company has a highly experienced management team and a strong board, who have decades of technology and management experience and worked in senior positions at many leading international financial businesses.
We are a vibrant, fun and exciting group of people that share a passion for technology and data. If you have what it takes to become a part of the SteelEye family, you have come to the right place. This is where you will find information about our people, culture and our current job opportunities.
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Amazon Web Services (AWS)PythonData 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.
PythonJob Summary
Role Overview
We are looking for an experienced Data Engineer with strong expertise in Python, ETL, Advanced SQL, CI/CD, DevOps, and Data Analytics. The ideal candidate should have hands-on experience designing and developing scalable data pipelines, transforming large datasets, and supporting data-driven applications.
Experience with Google Cloud Platform (GCP) will be an added advantage.
Key Responsibilities
- Design, develop, and maintain scalable ETL/ELT data pipelines using Python and SQL.
- Develop complex and optimized SQL queries, stored procedures, and data transformations.
- Build and maintain reliable data integration workflows across multiple data sources.
- Perform data cleansing, validation, transformation, and quality checks.
- Analyze data and provide insights to support business and technical requirements.
- Implement and maintain CI/CD pipelines for data engineering applications.
- Work with DevOps practices and tools to automate deployments, monitoring, and infrastructure processes.
- Troubleshoot data pipeline failures, performance issues, and production incidents.
- Optimize data processing workflows for performance, scalability, and reliability.
- Collaborate with Data Analysts, Data Scientists, Developers, and other stakeholders.
- Follow best practices for version control, testing, documentation, and deployment.
- Contribute to cloud-based data engineering initiatives, preferably on GCP.
Required Skills
- 5–7 years of hands-on experience in Data Engineering.
- Strong programming skills in Python.
- Strong expertise in Advanced SQL and database concepts.
- Hands-on experience with ETL/ELT processes and data pipelines.
- Good understanding of Data Warehousing and Data Modeling concepts.
- Experience with CI/CD practices and tools.
- Strong understanding of DevOps principles, automation, and deployment processes.
- Strong data analytics and problem-solving skills.
- Experience working with large datasets and performance optimization.
- Good understanding of Git/version control and software development best practices.
Good to Have
- Hands-on experience with Google Cloud Platform (GCP).
- Exposure to GCP data services such as BigQuery, Cloud Storage, Dataflow, Composer, or Pub/Sub.
- Experience with containerization/orchestration technologies such as Docker/Kubernetes.
- Experience with workflow orchestration tools such as Airflow.
- Knowledge of cloud-based data architecture and distributed data processing.
Preferred Candidate Profile
- Strong analytical and problem-solving abilities.
- Good communication and stakeholder management skills.
- Ability to work independently as well as in a collaborative team environment.
- Strong ownership of data pipelines and production systems.
- Candidates who can join at short notice are preferred.
Mandatory Skills
Data Engineer, Python , ETL, GCP, Advanced SQL, Strong Data Analytics skills, CICD, Devops
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
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.
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
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
PythonJob 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.
The recruiter has not been active on this job recently. You may apply but please expect a delayed response.
Amazon Web Services (AWS)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.
PythonJob 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.
PythonJob 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.
PythonAmazon Web Services (AWS)Skills Referential (Required knowledge, skills and abilities)
Technical Skills:
Python
Pyspark
SQL
ETL Aws, Azure, gcp
Follow CutshortShubham Vishwakarma
Full Stack Developer - Averlon