SENIOR DATA ENGINEER · LATENTVIEW ANALYTICS
DATA ENGINEERING & ANALYTICS PLATFORMS

Data pipelines.
Built for scale.

I’m Sachin. I build and optimize production-grade data pipelines and modern analytics platforms across cloud data warehouses, distributed processing, and ELT ecosystems.

My work spans marketing analytics, enterprise data migration, and end-to-end pipeline engineering—with a focus on reliability, maintainability, and making trusted data accessible to business teams.

Based in Bangalore, India Building data systems since 2022
THE SYSTEMS I BUILD DATA / PIPELINES / ANALYTICS
⇢ INGESTION

Source systems

Connectors · APIs
Batch data · Events

⌘ TRANSFORMATION

Data processing

SQL · Spark
dbt · Modeling

THE SHARED FOUNDATION Modern Data
Platforms
Ingest → Transform → Deliver
▤ ANALYTICS

Business insights

Curated data · BI
Metrics · Reporting

>_ OPERATIONS

Reliable delivery

Orchestration · DQ
Monitoring · SLAs

RELIABILITY DATA QUALITY OBSERVABILITY

Engineering focus · Conceptual map, not a client architecture

EXPERIENCE ACROSS
LatentViewFuturenseTech Mahindra
Scalable pipelines. Trusted data. Business impact.
01 / EXPERIENCE

Engineering data
from source to insight.

Experience across marketing analytics, enterprise migrations, and large-scale data processing—building reliable pipelines and analytics-ready datasets for business use cases.

01

LatentView Analytics logo LatentView Analytics

May 2024 — Present

Bangalore, India · Client: Autodesk

Sr. Analyst — Data Engineering

CURRENT

Working with Autodesk’s marketing analytics team on data engineering, building and maintaining scalable pipelines and curated datasets that support marketing measurement and reporting.

  • Pipeline engineering. Designed and orchestrated production-grade data pipelines using Airflow (Astronomer), Matillion, and Fivetran for reliable ingestion and processing across multiple sources.
  • ETL modernization. Led the migration of 60+ legacy Matillion ETL workflows to a modern dbt + Astronomer (Airflow) architecture, improving modularity, maintainability, and scalability.
  • SQL & data transformation. Developed and optimized complex SQL transformations on large datasets, improving query efficiency and supporting downstream analytics.
  • Reliability & monitoring. Implemented pipeline monitoring, alerting, debugging, and failure-handling mechanisms to support data reliability, availability, and SLA adherence.
  • Analytics enablement. Built and maintained curated, analytics-ready datasets for marketing use cases and Sigma reporting dashboards.
SnowflakedbtAstronomerAirflowMatillionFivetranSQL
02

Futurense Technologies logo Futurense Tech.

Apr 2023 — May 2024

Bangalore, India · Client: BluePi / Hero MotoCorp

Data Engineer

Worked on data migration, reporting modernization, and analytics projects involving Oracle, Snowflake, Azure, and distributed data processing technologies.

  • Hero MotoCorp migration. Contributed to migrating Oracle-based data and Informatica workflows to Snowflake using Fivetran, and supported migration of legacy Oracle OBIEE reports to Power BI after data validation using SnowSQL.
  • Migration automation. Developed a Python-based wrapper to help translate Informatica expressions into equivalent Snowflake SQL and supported XML-based analysis of Informatica jobs for Snowflake procedure development.
  • Data validation. Built Python validation checks to compare migrated tables across multiple data-quality metrics and generate table-level accuracy reports.
  • Uber analytics. Built an end-to-end analytics pipeline using Azure Data Factory, Azure storage, Snowflake, Hadoop, Hive, Sqoop, and PySpark; analyzed demand patterns and driver behavior and presented insights through Power BI.
PythonSnowflakeFivetranAzure Data FactoryPySparkPower BI
03

Tech Mahindra logo Tech Mahindra

Mar 2022 — Apr 2023

Pune, India

Associate Software Engineer

Started my professional career with technical training and foundational work across SAP ABAP, SQL, and programming.

  • SAP ABAP & Open SQL. Completed training and practical exercises using SAP GUI and ABAP, including data retrieval and reporting concepts.
  • Database foundations. Built foundational experience with SQL, MySQL, and SQL Server through coursework and hands-on exercises.
  • Programming. Developed programming fundamentals through Java and Python coursework and independent practice projects.
SAP ABAPOpen SQLMySQLSQL ServerJavaPython
02 / SELECTED WORK

Scalable systems.

Meaningful insights.

Selected professional projects spanning marketing analytics, migration automation, and end-to-end data engineering. Descriptions are intentionally high-level; client implementation details and source code are not published here.

01
MARKETING ANALYTICS / ETL MODERNIZATION LatentView · Autodesk

Marketing Analytics and Research System

A marketing analytics data platform supporting campaign measurement, customer engagement analysis, conversion tracking, and business reporting.

OWNERSHIPPipeline engineering & ETL modernization Professional project
Scope & engineering approach

What I worked on

Designed and maintained ingestion and transformation workflows using Fivetran, Matillion, Airflow (Astronomer), dbt, Snowflake, Python, and SQL. Built curated datasets for marketing analytics and Sigma reporting.

Key contribution

Led migration of 60+ legacy Matillion ETL workflows to a dbt + Astronomer architecture, with a focus on modularity, maintainability, monitoring, and operational reliability.

02
DATA MIGRATION / AUTOMATION Futurense · BluePi · Hero MotoCorp

Hero MotoCorp — Oracle to Snowflake migration

Modernization of Oracle-based data and Informatica workflows, with reporting migration and data validation as part of the Snowflake transition.

OWNERSHIPMigration support, automation & validation Professional project
Scope & engineering approach

What I worked on

Worked with existing Informatica logic and Oracle data, supporting migration to Snowflake using Fivetran and moving legacy Oracle OBIEE reports to Power BI after validation.

Automation & quality

Developed Python utilities to assist with Informatica-expression conversion and table-level validation across multiple data-quality metrics.

03
END-TO-END DATA ENGINEERING / BI Futurense Technologies

Uber Data Analytics

An analytics pipeline and reporting solution built to explore demand patterns and driver behavior from operational datasets.

OWNERSHIPData pipeline development & analytics Professional project
Scope & engineering approach

What I worked on

Used MySQL, Hadoop, Hive, Sqoop, and PySpark alongside Azure storage, Azure Data Factory, and Snowflake to build an end-to-end data processing workflow.

Business outcome

Analyzed demand patterns and driver behavior and presented actionable findings through Power BI dashboards to support business decision-making.

Professional project summaries only. Client-specific source code, credentials, and confidential implementation details are not shared.

CODE & EXPERIMENTS

More on GitHub.

Explore my public repositories and coding work.

Explore GitHub
DATA ENGINEERING/01

Data pipeline experiments

GitHub · Personal work

Hands-on exercises and experiments around SQL, Python, Spark, orchestration, and modern data engineering workflows.

IngestTransformValidateDeliver
DATA MODELING/02

SQL & dbt practice

GitHub · Learning and practice

Practice work focused on analytical SQL, transformations, data modeling patterns, and reusable ELT concepts.

SourcesModelsTestsAnalytics
DISTRIBUTED PROCESSING/03

Spark & big data exploration

GitHub · Learning and practice

Exploration of distributed data processing concepts using Spark, PySpark, Hadoop ecosystem tools, and analytical workloads.

DataPySparkAnalysis
PYTHON / AUTOMATION/04

Python utilities & automation

GitHub · Personal work

Small utilities and practice projects built to strengthen Python programming, automate repetitive tasks, and solve data-focused problems.

ProblemPythonAutomation
More repositories and coding work on GitHub ↗
03 / ENGINEERING EXPERTISE

Strong data foundations.
Reliable delivery.

I work across ingestion, transformation, orchestration, cloud data platforms, and analytics enablement—with an emphasis on dependable production pipelines.

01 /

Data engineering & ELT

Build and modernize ingestion and transformation workflows, moving data from multiple sources into reliable, analytics-ready platforms.

Ingestion · ETL/ELT · Data integration
Fivetran · Matillion · HVR · Informatica · dbt
02 /

Cloud data platforms

Work with cloud warehouses and data services to support scalable storage, processing, and downstream analytics.

Warehousing · Cloud storage · Processing
Snowflake · Azure · AWS · Databricks
03 /

Distributed data processing

Use distributed processing frameworks and big-data tools to transform and analyze large datasets efficiently.

Batch processing · Transformations · Analytics
Spark · PySpark · Hadoop · Hive
04 /

Orchestration & reliability

Orchestrate workflows and improve operational confidence through monitoring, alerting, debugging, and failure handling.

Scheduling · Monitoring · SLA adherence
Airflow · Astronomer · Azure Data Factory
05 /

SQL, Python & analytics

Develop transformations, automate data workflows, validate outputs, and prepare datasets for reporting and business analysis.

Query optimization · Validation · Reporting
SQL · Python · Pandas · NumPy · Sigma · Power BI
HOW I APPROACH ENGINEERING

Reliable data.
Useful outcomes.

Focus on data quality, maintainability, and operational reliability—not just moving data from one system to another.

A pipeline is only as useful as the data it delivers.
HOW I WORK

Build for the next run.

01

Make data dependable.

Use validation, monitoring, and failure handling to keep data accurate and available for downstream teams.

02

Prefer maintainable pipelines.

Use modular transformations and reusable orchestration patterns to simplify change and ongoing operations.

03

Connect engineering to outcomes.

Build curated datasets and reporting foundations that help analytics and business teams make informed decisions.

EDUCATION

SRM University

Bachelor’s degree
Electronics and Communication Engineering
2017 — 2021

TRAINING & ACCREDITATIONS
  • Databricks Lakehouse Fundamentals · Academy Accreditation
  • DBT Bootcamp: Zero to Hero · Udemy
  • Complete Hands-on Introduction to Apache Airflow 3 · Udemy
  • Python 3 Specialization · Coursera
  • Introduction to Git and GitHub · Udemy
  • SQL for Beginners · Udemy
LET’S CONNECT

Let’s talk
data engineering.

Data platforms, pipeline modernization, analytics engineering, or cloud data solutions.

sachinkace@gmail.com ↗ Résumé (PDF) ↗
Sachin Khajuria

Sachin Khajuria Bangalore, India